From 7a6427217aa0f6155f6fc0e6be2a95162d1f0ab9 Mon Sep 17 00:00:00 2001 From: anthology-assist Date: Thu, 21 Dec 2023 19:50:29 -0600 Subject: [PATCH 01/12] reingested insights workshop. --- data/xml/2023.insights.xml | 134 +++++++++++++++++++++++-------------- 1 file changed, 84 insertions(+), 50 deletions(-) diff --git a/data/xml/2023.insights.xml b/data/xml/2023.insights.xml index 5934a7bdf1..7b6ee1ba8a 100644 --- a/data/xml/2023.insights.xml +++ b/data/xml/2023.insights.xml @@ -1,8 +1,8 @@ - + - The Fourth Workshop on Insights from Negative Results in NLP + Workshop on Insights from Negative Results in NLP ShabnamTafreshi ArjunAkula JoãoSedoc @@ -13,11 +13,11 @@
Dubrovnik, Croatia
May 2023 - 2023.insights-1 + 2023.insights-1 insights - 2023.insights-1.0 + 2023.insights-1.0 insights-2023-insights @@ -25,12 +25,49 @@ AnyaBelzADAPT Research Centre, Dublin City University CraigThomsonUniversity of Aberdeen EhudReiterUniversity of Aberdeen + GavinAbercrombieHeriot-Watt University, + JoseAlonso-MoralUniversidade de Santiago de Compostela, Spain + MohammadArvanUniversity of Illinois Chicago + AnouckBraggaarTilburg University + MarkCieliebakZurich University of Applied Sciences, Switzerland + ElizabethClarkGoogle Research, US + KeesDeemterUtrecht University, Netherlands + TanviDinkarHeriot-Watt University + OndrejDušekCharles University Prague, Czechia + SteffenEgerBielefeld University, Germany + QixiangFangUtrecht University, Netherlands + MingqiGaoPeking University, China + AlbertGattUtrecht University, Netherlands + DimitraGkatziaEdinburgh Napier University, UK + JavierGonzález-CorbelleUniversidade de Santiago de Compostela, Spain + DirkHovyBocconi University, Italy + ManuelaHürlimannZurich University of Applied Sciences, Switzerland + TakumiItoTohoku University, Japan + JohnKelleherTechnological University Dublin, Ireland + FilipKlubickaTechnological University Dublin, Ireland + EmielKrahmerTilburg University, Netherlands + HuiyuanLaiGroningen University, Netherlands + Chrisvan der LeeTilburg University, Netherlands + YiruLiGroningen University, Netherlands + SaadMahamoodtrivago, Germany + MargotMieskesUniversity of Applied Sciences Darmstadt, Germany + EmielMiltenburgTilburg University, Netherlands + PabloMosteiroUtrecht University, Netherlands + MalvinaNissimGroningen University, Netherlands + NataliePardeUniversity of Illinois Chicago, US + OndrejPlátekCharles University Prague, Czechia + VerenaRieserHeriot-Watt University, UK + JieRuanPeking University, China + JoelTetreaultDataminr, US + AntonioToralGroningen University, Netherlands + XiaojunWanPeking University, China + LeoWannerUniversitat Pompeu Fabra, Spain + LewisWatsonEdinburgh Napier University, UK + DiyiYangGeorgia Tech, US 1-10 We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible. We present our results and findings, which include that just 13% of papers had (i) sufficiently low barriers to reproduction, and (ii) enough obtainable information, to be considered for reproduction, and that all but one of the experiments we selected for reproduction was discovered to have flaws that made the meaningfulness of conducting a reproduction questionable. As a result, we had to change our coordinated study design from a reproduce approach to a standardise-then-reproduce-twice approach. Our overall (negative) finding that the great majority of human evaluations in NLP is not repeatable and/or not reproducible and/or too flawed to justify reproduction, paints a dire picture, but presents an opportunity for a rethink about how to design and report human evaluations in NLP. - 2023.insights-1.1 + 2023.insights-1.1 belz-etal-2023-missing - <fixed-case>ERATE</fixed-case>: Efficient Retrieval Augmented Text Embeddings @@ -42,10 +79,8 @@ LouisMartinMeta AI 11-18 Embedding representations of text are useful for downstream natural language processing tasks. Several universal sentence representation methods have been proposed with a particular focus on self-supervised pre-training approaches to leverage the vast quantities of unlabelled data. However, there are two challenges for generating rich embedding representations for a new document. 1) The latest rich embedding generators are based on very large costly transformer-based architectures. 2) The rich embedding representation of a new document is limited to only the information provided without access to any explicit contextual and temporal information that could potentially further enrich the representation. We propose efficient retrieval-augmented text embeddings (ERATE) that tackles the first issue and offers a method to tackle the second issue. To the best of our knowledge, we are the first to incorporate retrieval to general purpose embeddings as a new paradigm, which we apply to the semantic similarity tasks of SentEval. Despite not reaching state-of-the-art performance, ERATE offers key insights that encourages future work into investigating the potential of retrieval-based embeddings. - 2023.insights-1.2 + 2023.insights-1.2 raina-etal-2023-erate - A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets @@ -59,23 +94,19 @@ ShafiqJotyNanyang Technological University; Salesforce AI Research JosipCarLKCMedicine, NTU Singapore 19-32 - 2023.insights-1.3 + Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a large number of domain experts for their creation. Thus, it is vital to ensure high-quality domain-specific training data. In this paper, we propose a framework for enhancing the data quality of original datasets. (Code and dataset are available at https://github.com/IvaBojic/framework). We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33{%/40{% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality. + 2023.insights-1.3 bojic-etal-2023-data - Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a large number of domain experts for their creation. Thus, it is vital to ensure high-quality domain-specific training data. In this paper, we propose a framework for enhancing the data quality of original datasets. (Code and dataset are available at https://github.com/IvaBojic/framework). We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33%/40% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality. - Encoding Sentence Position in Context-Aware Neural Machine Translation with Concatenation - LorenzoLupoLig - MarcoDinarelliLig + LorenzoLupoLIG + MarcoDinarelliLIG LaurentBesacierNaver Labs Europe 33-44 Context-aware translation can be achieved by processing a concatenation of consecutive sentences with the standard Transformer architecture. This paper investigates the intuitive idea of providing the model with explicit information about the position of the sentences contained in the concatenation window. We compare various methods to encode sentence positions into token representations, including novel methods. Our results show that the Transformer benefits from certain sentence position encoding methods on English to Russian translation, if trained with a context-discounted loss. However, the same benefits are not observed on English to German. Further empirical efforts are necessary to define the conditions under which the proposed approach is beneficial. - 2023.insights-1.4 + 2023.insights-1.4 lupo-etal-2023-encoding - <fixed-case>S</fixed-case>oc<fixed-case>BERT</fixed-case>: A Pretrained Model for Social Media Text @@ -83,33 +114,26 @@ AbeedSarkerEmory University 45-52 Pretrained language models (PLMs) on domain-specific data have been proven to be effective for in-domain natural language processing (NLP) tasks. Our work aimed to develop a language model which can be effective for the NLP tasks with the data from diverse social media platforms. We pretrained a language model on Twitter and Reddit posts in English consisting of 929M sequence blocks for 112K steps. We benchmarked our model and 3 transformer-based models—BERT, BERTweet, and RoBERTa on 40 social media text classification tasks. The results showed that although our model did not perform the best on all of the tasks, it outperformed the baseline model—BERT on most of the tasks, which illustrates the effectiveness of our model. Also, our work provides some insights of how to improve the efficiency of training PLMs. - 2023.insights-1.5 + 2023.insights-1.5 guo-sarker-2023-socbert - Edit Aware Representation Learning via <fixed-case>L</fixed-case>evenshtein Prediction - EdisonMarrese-TaylorNational Institute of Advanced Industrial Science and Technology (AIST) + EdisonMarrese-taylorNational Institute of Advanced Industrial Science and Technology (AIST) MachelReidGoogle AlfredoSolanoThe University of Tokyo 53-58 - We propose a novel approach that employs token-level Levenshtein operations to learn a continuous latent space of vector representations to capture the underlying semantic information with regard to the document editing process. Though our model outperforms strong baselines when fine-tuned on edit-centric tasks, it is unclear if these results are due to domain similarities between fine-tuning and pre-training data, suggesting that the benefits of our proposed approach over regular masked language-modelling pre-training are limited. - 2023.insights-1.6 + 2023.insights-1.6 marrese-taylor-etal-2023-edit - What changes when you randomly choose <fixed-case>BPE</fixed-case> merge operations? Not much. JonneSalevaBrandeis University ConstantineLignosBrandeis University 59-66 - We introduce two simple randomized variants of byte pair encoding (BPE) and explore whether randomizing the selection of merge operations substantially affects a downstream machine translation task. We focus on translation into morphologically rich languages, hypothesizing that this task may show sensitivity to the method of choosing subwords. Analysis using a Bayesian linear model indicates that one variant performs nearly indistinguishably compared to standard BPE while the other degrades performance less than we anticipated. We conclude that although standard BPE is widely used, there exists an interesting universe of potential variations on it worth investigating. Our code is available at: https://github.com/bltlab/random-bpe. - 2023.insights-1.7 + We introduce two simple randomized variants of byte pair encoding (BPE) and explore whether randomizing the selection of merge operations substantially affects a downstream machine translation task. We focus on translation into morphologically rich languages, hypothesizing that this task may show sensitivity to the method of choosing subwords. Analysis using a Bayesian linear model indicates that one variant performs nearly indistinguishably compared to standard BPE while the other degrades performance less than we anticipated. We conclude that although standard BPE is widely used, there exists an interesting universe of potential variations on it worth investigating. Our code is available at: https://github.com/bltlab/random-bpe. + 2023.insights-1.7 saleva-lignos-2023-changes - Hiding in Plain Sight: Insights into Abstractive Text Summarization @@ -118,47 +142,39 @@ NiranjanPedanekarTCS Research 67-74 In recent years, there has been growing interest in the field of abstractive text summarization with focused contributions in relevant model architectures, datasets, and evaluation metrics. Despite notable research advances, previous works have identified certain limitations concerning the quality of datasets and the effectiveness of evaluation techniques for generated summaries. In this context, we examine these limitations further with the help of three quality measures, namely, Information Coverage, Entity Hallucination, and Summarization Complexity. As a part of this work, we investigate two widely used datasets (XSUM and CNNDM) and three existing models (BART, PEGASUS, and BRIO) and report our findings. Some key insights are: 1) Cumulative ROUGE score is an inappropriate evaluation measure since few high-scoring samples dominate the overall performance, 2) Existing summarization models have limited capability for information coverage and hallucinate to generate factual information, and 3) Compared to the model generated summaries, the reference summaries have lowest information coverage and highest entity hallucinations reiterating the need of new and better reference summaries. - 2023.insights-1.8 + 2023.insights-1.8 srivastava-etal-2023-hiding - Annotating <fixed-case>P</fixed-case>ub<fixed-case>M</fixed-case>ed Abstracts with <fixed-case>M</fixed-case>e<fixed-case>SH</fixed-case> Headings using Graph Neural Network - Faizan EMustafaQuibiq GmbH + FaizanMustafaQuibiq GmbH RafikaBoutalbiUniversität Stuttgart AnastasiiaIurshinaUniversität Stuttgart 75-81 The number of scientific publications in the biomedical domain is continuously increasing with time. An efficient system for indexing these publications is required to make the information accessible according to the user’s information needs. Task 10a of the BioASQ challenge aims to classify PubMed articles according to the MeSH ontology so that new publications can be grouped with similar preexisting publications in the field without the assistance of time-consuming and costly annotations by human annotators. In this work, we use Graph Neural Network (GNN) in the link prediction setting to exploit potential graph-structured information present in the dataset which could otherwise be neglected by transformer-based models. Additionally, we provide error analysis and a plausible reason for the substandard performance achieved by GNN. - 2023.insights-1.9 + 2023.insights-1.9 mustafa-etal-2023-annotating - - Do not Trust the Experts: How the Lack of Standard Complicates <fixed-case>NLP</fixed-case> for Historical <fixed-case>I</fixed-case>rish + Do not Trust the Experts - How the Lack of Standard Complicates <fixed-case>NLP</fixed-case> for Historical <fixed-case>I</fixed-case>rish OksanaDerezaInsight Centre for Data Analytics, Data Science Institute, University of Galway TheodorusFransenData Science Institute, Insight Centre for Data Analytics, National University of Ireland, Galway John P.MccraeInsight Center for Data Analytics, National University of Ireland Galway 82-87 - In this paper, we describe how we unearthed some fundamental problems while building an analogy dataset modelled on BATS (Gladkova et al., 2016) to evaluate historical Irish embeddings on their ability to detect orthographic, morphological and semantic similarity. The performance of our models in the analogy task was extremely poor regardless of the architecture, hyperparameters and evaluation metrics, while the qualitative evaluation revealed positive tendencies. We argue that low agreement between field experts on fundamental lexical and orthographic issues, and the lack of a unified editorial standard in available resources make it impossible to build reliable evaluation datasets for computational models and obtain interpretable results. We emphasise the need for such a standard, particularly for NLP applications, and prompt Celticists and historical linguists to engage in further discussion. We would also like to draw NLP scholars’ attention to the role of data and its (extra)linguistic properties in testing new models, technologies and evaluation scenarios. - 2023.insights-1.10 + In this paper, we describe how we unearthed some fundamental problems while building an analogy dataset modelled on BATS (Gladkova et al., 2016) to evaluate historical Irish embeddings on their ability to detect orthographic, morphological and semantic similarity.performance of our models in the analogy task was extremely poor regardless of the architecture, hyperparameters and evaluation metrics, while the qualitative evaluation revealed positive tendencies. argue that low agreement between field experts on fundamental lexical and orthographic issues, and the lack of a unified editorial standard in available resources make it impossible to build reliable evaluation datasets for computational models and obtain interpretable results. We emphasise the need for such a standard, particularly for NLP applications, and prompt Celticists and historical linguists to engage in further discussion. We would also like to draw NLP scholars’ attention to the role of data and its (extra)linguistic properties in testing new models, technologies and evaluation scenarios. + 2023.insights-1.10 dereza-etal-2023-trust - Exploring the Reasons for Non-generalizability of <fixed-case>KBQA</fixed-case> systems SopanKhoslaAmazon Web Services, Amazon Inc RitamDuttCarnegie Mellon University - VinayshekharBannihatti KumarAws Ai + VinayshekharBannihatti KumarAWS AI RashmiGangadharaiahAWS AI, Amazon 88-93 Recent research has demonstrated impressive generalization capabilities of several Knowledge Base Question Answering (KBQA) models on the GrailQA dataset. We inspect whether these models can generalize to other datasets in a zero-shot setting. We notice a significant drop in performance and investigate the causes for the same. We observe that the models are dependent not only on the structural complexity of the questions, but also on the linguistic styles of framing a question. Specifically, the linguistic dimensions corresponding to explicitness, readability, coherence, and grammaticality have a significant impact on the performance of state-of-the-art KBQA models. Overall our results showcase the brittleness of such models and the need for creating generalizable systems. - 2023.insights-1.11 + 2023.insights-1.11 khosla-etal-2023-exploring - An Empirical Study on Active Learning for Multi-label Text Classification @@ -166,10 +182,28 @@ MingLiuDeakin University 94-102 Active learning has been widely used in the task of text classification for its ability to select the most valuable samples to annotate while improving the model performance. However, the efficiency of active learning in multi-label text classification tasks has been under-explored due to the label imbalanceness problem. In this paper, we conduct an empirical study of active learning on multi-label text classification and evaluate the efficiency of five active learning strategies on six multi-label text classification tasks. The experiments show that some strategies in the single-label setting especially in imbalanced datasets. - 2023.insights-1.12 + 2023.insights-1.12 wang-liu-2023-empirical - + + What Does <fixed-case>BERT</fixed-case> actually Learn about Event Coreference? Probing Structural Information in a Fine-Tuned <fixed-case>D</fixed-case>utch Language Model + LoicDe LangheGhent University + OrpheeDe ClercqLT3, Ghent University + VeroniqueHosteLT3, Ghent University + 103-108 + We probe structural and discourse aspects of coreferential relationships in a fine-tuned Dutch BERT event coreference model. Previous research has suggested that no such knowledge is encoded in BERT-based models and the classification of coreferential relationships ultimately rests on outward lexical similarity. While we show that BERT can encode a (very) limited number of these discourse aspects (thus disproving assumptions in earlier research), we also note that knowledge of many structural features of coreferential relationships is absent from the encodings generated by the fine-tuned BERT model. + 2023.insights-1.13 + de-langhe-etal-2023-bert + + + Estimating Numbers without Regression + AvijitThawaniUniversity of Southern California + JayPujaraUniversity of Southern California + AshwinKalyanAllen Institute for Artificial Intelligence (AI2) + 109-116 + Despite recent successes in language models, their ability to represent numbers is insufficient. Humans conceptualize numbers based on their magnitudes, effectively projecting them on a number line; whereas subword tokenization fails to explicitly capture magnitude by splitting numbers into arbitrary chunks. To alleviate this shortcoming, alternative approaches have been proposed that modify numbers at various stages of the language modeling pipeline. These methods change either the (1) notation in which numbers are written ({eg scientific vs decimal), the (2) vocabulary used to represent numbers or the entire (3) architecture of the underlying language model, to directly regress to a desired number. Previous work suggests that architectural change helps achieve state-of-the-art on number estimation but we find an insightful ablation - changing the model”s vocabulary instead ({eg introduce a new token for numbers in range 10-100) is a far better trade-off. In the context of masked number prediction, a carefully designed tokenization scheme is both the simplest to implement and sufficient, {ie with similar performance to the state-of-the-art approach that requires making significant architectural changes. Finally, we report similar trends on the downstream task of numerical fact estimation (for Fermi Problems) and discuss reasons behind our findings. + 2023.insights-1.14 + thawani-etal-2023-estimating From dabee91332dc038f921d00a1e1de5ba96477346e Mon Sep 17 00:00:00 2001 From: anthology assist Date: Sun, 31 Dec 2023 13:41:35 -0600 Subject: [PATCH 02/12] fixed error. --- data/xml/2023.insights.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data/xml/2023.insights.xml b/data/xml/2023.insights.xml index 7b6ee1ba8a..fe5f948527 100644 --- a/data/xml/2023.insights.xml +++ b/data/xml/2023.insights.xml @@ -94,7 +94,7 @@ ShafiqJotyNanyang Technological University; Salesforce AI Research JosipCarLKCMedicine, NTU Singapore 19-32 - Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a large number of domain experts for their creation. Thus, it is vital to ensure high-quality domain-specific training data. In this paper, we propose a framework for enhancing the data quality of original datasets. (Code and dataset are available at https://github.com/IvaBojic/framework). We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33{%/40{% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality. + Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a large number of domain experts for their creation. Thus, it is vital to ensure high-quality domain-specific training data. In this paper, we propose a framework for enhancing the data quality of original datasets. (Code and dataset are available at https://github.com/IvaBojic/framework). We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33%/40% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality. 2023.insights-1.3 bojic-etal-2023-data From 6c93c44058c1bd69fa9809055d6f5417e4d64d59 Mon Sep 17 00:00:00 2001 From: anthology assist Date: Mon, 1 Jan 2024 20:46:35 -0600 Subject: [PATCH 03/12] inserted license comment. --- python/acl_anthology/__init__.py | 2 +- python/acl_anthology/anthology.py | 2 +- python/acl_anthology/collections/__init__.py | 2 +- python/acl_anthology/collections/collection.py | 2 +- python/acl_anthology/collections/event.py | 2 +- python/acl_anthology/collections/eventindex.py | 2 +- python/acl_anthology/collections/index.py | 2 +- python/acl_anthology/collections/paper.py | 2 +- python/acl_anthology/collections/types.py | 2 +- python/acl_anthology/collections/volume.py | 2 +- python/acl_anthology/config.py | 2 +- python/acl_anthology/constants.py | 2 +- python/acl_anthology/containers.py | 2 +- python/acl_anthology/exceptions.py | 2 +- python/acl_anthology/files.py | 2 +- python/acl_anthology/people/__init__.py | 2 +- python/acl_anthology/people/index.py | 2 +- python/acl_anthology/people/name.py | 2 +- python/acl_anthology/people/person.py | 2 +- python/acl_anthology/sigs.py | 2 +- python/acl_anthology/text/__init__.py | 2 +- python/acl_anthology/text/markuptext.py | 2 +- python/acl_anthology/text/texmath.py | 2 +- python/acl_anthology/utils/__init__.py | 2 +- python/acl_anthology/utils/git.py | 2 +- python/acl_anthology/utils/ids.py | 2 +- python/acl_anthology/utils/latex.py | 2 +- python/acl_anthology/utils/logging.py | 2 +- python/acl_anthology/utils/text.py | 2 +- python/acl_anthology/utils/xml.py | 2 +- python/acl_anthology/venues.py | 2 +- python/benchmarks/bench_attrs.py | 2 +- python/benchmarks/bench_sanitycheck.py | 2 +- python/benchmarks/bench_utils.py | 2 +- python/benchmarks/bench_xml_markup.py | 2 +- python/benchmarks/bench_xml_names.py | 2 +- python/benchmarks/bench_xml_parsing.py | 2 +- python/tests/anthology_integration_test.py | 2 +- python/tests/anthology_test.py | 2 +- python/tests/collections/collection_test.py | 2 +- python/tests/collections/collectionindex_test.py | 2 +- python/tests/collections/event_test.py | 2 +- python/tests/collections/eventindex_test.py | 2 +- python/tests/collections/paper_test.py | 2 +- python/tests/collections/volume_test.py | 2 +- python/tests/conftest.py | 2 +- python/tests/containers_test.py | 2 +- python/tests/files_test.py | 2 +- python/tests/people/name_test.py | 2 +- python/tests/people/person_test.py | 2 +- python/tests/people/personindex_test.py | 2 +- python/tests/sigs_test.py | 2 +- python/tests/text/markuptext_test.py | 2 +- python/tests/text/texmath_test.py | 2 +- python/tests/utils/ids_test.py | 2 +- python/tests/utils/latex_test.py | 2 +- python/tests/utils/logging_test.py | 2 +- python/tests/utils/text_test.py | 2 +- python/tests/utils/xml_test.py | 2 +- python/tests/venues_test.py | 2 +- 60 files changed, 60 insertions(+), 60 deletions(-) diff --git a/python/acl_anthology/__init__.py b/python/acl_anthology/__init__.py index 382bf090d8..5d1c5dd731 100644 --- a/python/acl_anthology/__init__.py +++ b/python/acl_anthology/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/anthology.py b/python/acl_anthology/anthology.py index ebacdc3602..0761e017c7 100644 --- a/python/acl_anthology/anthology.py +++ b/python/acl_anthology/anthology.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/collections/__init__.py b/python/acl_anthology/collections/__init__.py index df003b3bc1..a8d8b30f21 100644 --- a/python/acl_anthology/collections/__init__.py +++ b/python/acl_anthology/collections/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/collections/collection.py b/python/acl_anthology/collections/collection.py index d99dda23b5..daa7c378ca 100644 --- a/python/acl_anthology/collections/collection.py +++ b/python/acl_anthology/collections/collection.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/collections/event.py b/python/acl_anthology/collections/event.py index 750bc8f747..b6c7ae1fc5 100644 --- a/python/acl_anthology/collections/event.py +++ b/python/acl_anthology/collections/event.py @@ -1,5 +1,5 @@ # Copyright 2022 Matt Post -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/collections/eventindex.py b/python/acl_anthology/collections/eventindex.py index ca701f9a88..43140dfd28 100644 --- a/python/acl_anthology/collections/eventindex.py +++ b/python/acl_anthology/collections/eventindex.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/collections/index.py b/python/acl_anthology/collections/index.py index 1bf257523e..85904f2c97 100644 --- a/python/acl_anthology/collections/index.py +++ b/python/acl_anthology/collections/index.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/collections/paper.py b/python/acl_anthology/collections/paper.py index 4ac25483b0..acbfb3448b 100644 --- a/python/acl_anthology/collections/paper.py +++ b/python/acl_anthology/collections/paper.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/collections/types.py b/python/acl_anthology/collections/types.py index ef26af6639..e7ff137832 100644 --- a/python/acl_anthology/collections/types.py +++ b/python/acl_anthology/collections/types.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/collections/volume.py b/python/acl_anthology/collections/volume.py index c2d65c0928..43735fb646 100644 --- a/python/acl_anthology/collections/volume.py +++ b/python/acl_anthology/collections/volume.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/config.py b/python/acl_anthology/config.py index 4d9f0cbb0c..5d371e05e8 100644 --- a/python/acl_anthology/config.py +++ b/python/acl_anthology/config.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/constants.py b/python/acl_anthology/constants.py index 42d0800088..67fe017a16 100644 --- a/python/acl_anthology/constants.py +++ b/python/acl_anthology/constants.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/containers.py b/python/acl_anthology/containers.py index 58d1b3e92a..98a67decdd 100644 --- a/python/acl_anthology/containers.py +++ b/python/acl_anthology/containers.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/exceptions.py b/python/acl_anthology/exceptions.py index 0d16951c66..74830b36ef 100644 --- a/python/acl_anthology/exceptions.py +++ b/python/acl_anthology/exceptions.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/files.py b/python/acl_anthology/files.py index 0347356f9b..f8fded4fcc 100644 --- a/python/acl_anthology/files.py +++ b/python/acl_anthology/files.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/people/__init__.py b/python/acl_anthology/people/__init__.py index ba7e72f628..49063fa6de 100644 --- a/python/acl_anthology/people/__init__.py +++ b/python/acl_anthology/people/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/people/index.py b/python/acl_anthology/people/index.py index fb14469bc3..41b25cb993 100644 --- a/python/acl_anthology/people/index.py +++ b/python/acl_anthology/people/index.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/people/name.py b/python/acl_anthology/people/name.py index 4876dd0326..116dd17240 100644 --- a/python/acl_anthology/people/name.py +++ b/python/acl_anthology/people/name.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/people/person.py b/python/acl_anthology/people/person.py index 1f123985eb..075086c476 100644 --- a/python/acl_anthology/people/person.py +++ b/python/acl_anthology/people/person.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/sigs.py b/python/acl_anthology/sigs.py index 3475ca812b..9a835d2d43 100644 --- a/python/acl_anthology/sigs.py +++ b/python/acl_anthology/sigs.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/text/__init__.py b/python/acl_anthology/text/__init__.py index 6aa2046421..9622479a9d 100644 --- a/python/acl_anthology/text/__init__.py +++ b/python/acl_anthology/text/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/text/markuptext.py b/python/acl_anthology/text/markuptext.py index 2d81f67fd4..facc0e1c2e 100644 --- a/python/acl_anthology/text/markuptext.py +++ b/python/acl_anthology/text/markuptext.py @@ -1,4 +1,4 @@ -# Copyright 2019-2023 Marcel Bollmann +# Copyright 2019-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/text/texmath.py b/python/acl_anthology/text/texmath.py index 2dee8fbec1..4af8bb5cb9 100644 --- a/python/acl_anthology/text/texmath.py +++ b/python/acl_anthology/text/texmath.py @@ -1,4 +1,4 @@ -# Copyright 2019-2023 Marcel Bollmann +# Copyright 2019-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/utils/__init__.py b/python/acl_anthology/utils/__init__.py index 5d4173b990..fcf9e8520d 100644 --- a/python/acl_anthology/utils/__init__.py +++ b/python/acl_anthology/utils/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/utils/git.py b/python/acl_anthology/utils/git.py index f8891ee2c1..f203c75eca 100644 --- a/python/acl_anthology/utils/git.py +++ b/python/acl_anthology/utils/git.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/utils/ids.py b/python/acl_anthology/utils/ids.py index 6e4434fe04..b70519e9a5 100644 --- a/python/acl_anthology/utils/ids.py +++ b/python/acl_anthology/utils/ids.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/utils/latex.py b/python/acl_anthology/utils/latex.py index b1821357d7..a3c492f4de 100644 --- a/python/acl_anthology/utils/latex.py +++ b/python/acl_anthology/utils/latex.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/utils/logging.py b/python/acl_anthology/utils/logging.py index e6119ce213..ae1fabaabe 100644 --- a/python/acl_anthology/utils/logging.py +++ b/python/acl_anthology/utils/logging.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/utils/text.py b/python/acl_anthology/utils/text.py index d030a44a11..c4abda31ac 100644 --- a/python/acl_anthology/utils/text.py +++ b/python/acl_anthology/utils/text.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/utils/xml.py b/python/acl_anthology/utils/xml.py index 87942cb7da..f52f512afd 100644 --- a/python/acl_anthology/utils/xml.py +++ b/python/acl_anthology/utils/xml.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/acl_anthology/venues.py b/python/acl_anthology/venues.py index 16b05e822a..6043b4f465 100644 --- a/python/acl_anthology/venues.py +++ b/python/acl_anthology/venues.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/benchmarks/bench_attrs.py b/python/benchmarks/bench_attrs.py index dd2c4121a4..194224afce 100644 --- a/python/benchmarks/bench_attrs.py +++ b/python/benchmarks/bench_attrs.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/benchmarks/bench_sanitycheck.py b/python/benchmarks/bench_sanitycheck.py index 1659bc51f6..a22d91fc9e 100644 --- a/python/benchmarks/bench_sanitycheck.py +++ b/python/benchmarks/bench_sanitycheck.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/benchmarks/bench_utils.py b/python/benchmarks/bench_utils.py index 1953223505..e7a69861fe 100644 --- a/python/benchmarks/bench_utils.py +++ b/python/benchmarks/bench_utils.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/benchmarks/bench_xml_markup.py b/python/benchmarks/bench_xml_markup.py index a3b4873667..47185ff074 100644 --- a/python/benchmarks/bench_xml_markup.py +++ b/python/benchmarks/bench_xml_markup.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/benchmarks/bench_xml_names.py b/python/benchmarks/bench_xml_names.py index 401f94fbe1..d729eba94c 100644 --- a/python/benchmarks/bench_xml_names.py +++ b/python/benchmarks/bench_xml_names.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/benchmarks/bench_xml_parsing.py b/python/benchmarks/bench_xml_parsing.py index 219a425bfb..c1c9b3a697 100644 --- a/python/benchmarks/bench_xml_parsing.py +++ b/python/benchmarks/bench_xml_parsing.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/anthology_integration_test.py b/python/tests/anthology_integration_test.py index 89d1371ceb..225ade4dc7 100644 --- a/python/tests/anthology_integration_test.py +++ b/python/tests/anthology_integration_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/anthology_test.py b/python/tests/anthology_test.py index c22ec60450..fa913fb168 100644 --- a/python/tests/anthology_test.py +++ b/python/tests/anthology_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/collections/collection_test.py b/python/tests/collections/collection_test.py index 22246e61f9..f4509648da 100644 --- a/python/tests/collections/collection_test.py +++ b/python/tests/collections/collection_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/collections/collectionindex_test.py b/python/tests/collections/collectionindex_test.py index 06532cc62f..c7305df5d5 100644 --- a/python/tests/collections/collectionindex_test.py +++ b/python/tests/collections/collectionindex_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/collections/event_test.py b/python/tests/collections/event_test.py index 9ee4bf1980..d311857e2a 100644 --- a/python/tests/collections/event_test.py +++ b/python/tests/collections/event_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/collections/eventindex_test.py b/python/tests/collections/eventindex_test.py index 4433a7b3b3..7bb87cb841 100644 --- a/python/tests/collections/eventindex_test.py +++ b/python/tests/collections/eventindex_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/collections/paper_test.py b/python/tests/collections/paper_test.py index 77feaf087b..f21f2a032e 100644 --- a/python/tests/collections/paper_test.py +++ b/python/tests/collections/paper_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/collections/volume_test.py b/python/tests/collections/volume_test.py index 23914554e2..d111b94eb3 100644 --- a/python/tests/collections/volume_test.py +++ b/python/tests/collections/volume_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/conftest.py b/python/tests/conftest.py index d7c342bd6d..c09ab19516 100644 --- a/python/tests/conftest.py +++ b/python/tests/conftest.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/containers_test.py b/python/tests/containers_test.py index 342ee346ed..d0bdf30dae 100644 --- a/python/tests/containers_test.py +++ b/python/tests/containers_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/files_test.py b/python/tests/files_test.py index f815332f9e..b08fbab9be 100644 --- a/python/tests/files_test.py +++ b/python/tests/files_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/people/name_test.py b/python/tests/people/name_test.py index a33dfbed78..1bb1d4c9d9 100644 --- a/python/tests/people/name_test.py +++ b/python/tests/people/name_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/people/person_test.py b/python/tests/people/person_test.py index 36559610ff..f99ba3b885 100644 --- a/python/tests/people/person_test.py +++ b/python/tests/people/person_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/people/personindex_test.py b/python/tests/people/personindex_test.py index 2a9a58db4b..a653189dd3 100644 --- a/python/tests/people/personindex_test.py +++ b/python/tests/people/personindex_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/sigs_test.py b/python/tests/sigs_test.py index 0882203b22..aef1817289 100644 --- a/python/tests/sigs_test.py +++ b/python/tests/sigs_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/text/markuptext_test.py b/python/tests/text/markuptext_test.py index 78a4a2f400..b53f63dccd 100644 --- a/python/tests/text/markuptext_test.py +++ b/python/tests/text/markuptext_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/text/texmath_test.py b/python/tests/text/texmath_test.py index c5bc9779fb..2ad78493f6 100644 --- a/python/tests/text/texmath_test.py +++ b/python/tests/text/texmath_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/utils/ids_test.py b/python/tests/utils/ids_test.py index c87e962c27..0b77937e62 100644 --- a/python/tests/utils/ids_test.py +++ b/python/tests/utils/ids_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/utils/latex_test.py b/python/tests/utils/latex_test.py index 29a436271f..9f9174d67b 100644 --- a/python/tests/utils/latex_test.py +++ b/python/tests/utils/latex_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/utils/logging_test.py b/python/tests/utils/logging_test.py index bf5c773b20..f9939c85ce 100644 --- a/python/tests/utils/logging_test.py +++ b/python/tests/utils/logging_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/utils/text_test.py b/python/tests/utils/text_test.py index 32c7b7e5a1..754b0d6dbf 100644 --- a/python/tests/utils/text_test.py +++ b/python/tests/utils/text_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/utils/xml_test.py b/python/tests/utils/xml_test.py index 88acbd0c6e..f19d0d619b 100644 --- a/python/tests/utils/xml_test.py +++ b/python/tests/utils/xml_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/python/tests/venues_test.py b/python/tests/venues_test.py index a2e2266118..d918ba593e 100644 --- a/python/tests/venues_test.py +++ b/python/tests/venues_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Marcel Bollmann +# Copyright 2023-2024 Marcel Bollmann # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. From 6f132c1cda032f8a1d765c8396237d92d8957424 Mon Sep 17 00:00:00 2001 From: anthology assist Date: Mon, 1 Jan 2024 20:48:38 -0600 Subject: [PATCH 04/12] fixed insights xml issue. --- data/xml/2023.insights.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data/xml/2023.insights.xml b/data/xml/2023.insights.xml index fe5f948527..30270cad8c 100644 --- a/data/xml/2023.insights.xml +++ b/data/xml/2023.insights.xml @@ -201,7 +201,7 @@ JayPujaraUniversity of Southern California AshwinKalyanAllen Institute for Artificial Intelligence (AI2) 109-116 - Despite recent successes in language models, their ability to represent numbers is insufficient. Humans conceptualize numbers based on their magnitudes, effectively projecting them on a number line; whereas subword tokenization fails to explicitly capture magnitude by splitting numbers into arbitrary chunks. To alleviate this shortcoming, alternative approaches have been proposed that modify numbers at various stages of the language modeling pipeline. These methods change either the (1) notation in which numbers are written ({eg scientific vs decimal), the (2) vocabulary used to represent numbers or the entire (3) architecture of the underlying language model, to directly regress to a desired number. Previous work suggests that architectural change helps achieve state-of-the-art on number estimation but we find an insightful ablation - changing the model”s vocabulary instead ({eg introduce a new token for numbers in range 10-100) is a far better trade-off. In the context of masked number prediction, a carefully designed tokenization scheme is both the simplest to implement and sufficient, {ie with similar performance to the state-of-the-art approach that requires making significant architectural changes. Finally, we report similar trends on the downstream task of numerical fact estimation (for Fermi Problems) and discuss reasons behind our findings. + Despite recent successes in language models, their ability to represent numbers is insufficient. Humans conceptualize numbers based on their magnitudes, effectively projecting them on a number line; whereas subword tokenization fails to explicitly capture magnitude by splitting numbers into arbitrary chunks. To alleviate this shortcoming, alternative approaches have been proposed that modify numbers at various stages of the language modeling pipeline. These methods change either the (1) notation in which numbers are written (eg scientific vs decimal), the (2) vocabulary used to represent numbers or the entire (3) architecture of the underlying language model, to directly regress to a desired number. Previous work suggests that architectural change helps achieve state-of-the-art on number estimation but we find an insightful ablation - changing the model”s vocabulary instead ({eg introduce a new token for numbers in range 10-100) is a far better trade-off. In the context of masked number prediction, a carefully designed tokenization scheme is both the simplest to implement and sufficient, ie with similar performance to the state-of-the-art approach that requires making significant architectural changes. Finally, we report similar trends on the downstream task of numerical fact estimation (for Fermi Problems) and discuss reasons behind our findings. 2023.insights-1.14 thawani-etal-2023-estimating From fe7186e65d658b26052e49995806ea5ec3741ea2 Mon Sep 17 00:00:00 2001 From: anthology assist Date: Mon, 1 Jan 2024 21:15:02 -0600 Subject: [PATCH 05/12] fix xml issue. --- data/xml/2023.insights.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data/xml/2023.insights.xml b/data/xml/2023.insights.xml index 30270cad8c..a000b4cf68 100644 --- a/data/xml/2023.insights.xml +++ b/data/xml/2023.insights.xml @@ -201,7 +201,7 @@ JayPujaraUniversity of Southern California AshwinKalyanAllen Institute for Artificial Intelligence (AI2) 109-116 - Despite recent successes in language models, their ability to represent numbers is insufficient. Humans conceptualize numbers based on their magnitudes, effectively projecting them on a number line; whereas subword tokenization fails to explicitly capture magnitude by splitting numbers into arbitrary chunks. To alleviate this shortcoming, alternative approaches have been proposed that modify numbers at various stages of the language modeling pipeline. These methods change either the (1) notation in which numbers are written (eg scientific vs decimal), the (2) vocabulary used to represent numbers or the entire (3) architecture of the underlying language model, to directly regress to a desired number. Previous work suggests that architectural change helps achieve state-of-the-art on number estimation but we find an insightful ablation - changing the model”s vocabulary instead ({eg introduce a new token for numbers in range 10-100) is a far better trade-off. In the context of masked number prediction, a carefully designed tokenization scheme is both the simplest to implement and sufficient, ie with similar performance to the state-of-the-art approach that requires making significant architectural changes. Finally, we report similar trends on the downstream task of numerical fact estimation (for Fermi Problems) and discuss reasons behind our findings. + Despite recent successes in language models, their ability to represent numbers is insufficient. Humans conceptualize numbers based on their magnitudes, effectively projecting them on a number line; whereas subword tokenization fails to explicitly capture magnitude by splitting numbers into arbitrary chunks. To alleviate this shortcoming, alternative approaches have been proposed that modify numbers at various stages of the language modeling pipeline. These methods change either the (1) notation in which numbers are written (eg scientific vs decimal), the (2) vocabulary used to represent numbers or the entire (3) architecture of the underlying language model, to directly regress to a desired number. Previous work suggests that architectural change helps achieve state-of-the-art on number estimation but we find an insightful ablation - changing the model”s vocabulary instead (eg introduce a new token for numbers in range 10-100) is a far better trade-off. In the context of masked number prediction, a carefully designed tokenization scheme is both the simplest to implement and sufficient, ie with similar performance to the state-of-the-art approach that requires making significant architectural changes. Finally, we report similar trends on the downstream task of numerical fact estimation (for Fermi Problems) and discuss reasons behind our findings. 2023.insights-1.14 thawani-etal-2023-estimating From 16ca7108b33c8fe7ba6481982f63eeb2c38c055c Mon Sep 17 00:00:00 2001 From: anthology-assist Date: Tue, 19 Mar 2024 20:48:16 -0500 Subject: [PATCH 06/12] minor update. --- data/xml/2023.insights.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data/xml/2023.insights.xml b/data/xml/2023.insights.xml index 1d15a3243b..47f9d3729c 100644 --- a/data/xml/2023.insights.xml +++ b/data/xml/2023.insights.xml @@ -2,7 +2,7 @@ - Workshop on Insights from Negative Results in NLP + Proceedings of the Fourth Workshop on Insights from Negative Results in NLP ShabnamTafreshi ArjunAkula JoãoSedoc From d130dde84c6180c0a022d54e382ecc69ea9d9c82 Mon Sep 17 00:00:00 2001 From: anthology-assist Date: Tue, 19 Mar 2024 20:59:51 -0500 Subject: [PATCH 07/12] minor update. --- data/xml/2023.insights.xml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/data/xml/2023.insights.xml b/data/xml/2023.insights.xml index 47f9d3729c..aa9a3fc215 100644 --- a/data/xml/2023.insights.xml +++ b/data/xml/2023.insights.xml @@ -85,6 +85,8 @@ Embedding representations of text are useful for downstream natural language processing tasks. Several universal sentence representation methods have been proposed with a particular focus on self-supervised pre-training approaches to leverage the vast quantities of unlabelled data. However, there are two challenges for generating rich embedding representations for a new document. 1) The latest rich embedding generators are based on very large costly transformer-based architectures. 2) The rich embedding representation of a new document is limited to only the information provided without access to any explicit contextual and temporal information that could potentially further enrich the representation. We propose efficient retrieval-augmented text embeddings (ERATE) that tackles the first issue and offers a method to tackle the second issue. To the best of our knowledge, we are the first to incorporate retrieval to general purpose embeddings as a new paradigm, which we apply to the semantic similarity tasks of SentEval. Despite not reaching state-of-the-art performance, ERATE offers key insights that encourages future work into investigating the potential of retrieval-based embeddings. 2023.insights-1.2 raina-etal-2023-erate +