[automated] Update metadata from Papers with Code

data/xml/2022.coling.xml

@@ -3224,6 +3224,8 @@
       <abstract>Automated essay scoring (AES) involves the prediction of a score relating to the writing quality of an essay. Most existing works in AES utilize regression objectives or ranking objectives respectively. However, the two types of methods are highly complementary. To this end, in this paper we take inspiration from contrastive learning and propose a novel unified Neural Pairwise Contrastive Regression (NPCR) model in which both objectives are optimized simultaneously as a single loss. Specifically, we first design a neural pairwise ranking model to guarantee the global ranking order in a large list of essays, and then we further extend this pairwise ranking model to predict the relative scores between an input essay and several reference essays. Additionally, a multi-sample voting strategy is employed for inference. We use Quadratic Weighted Kappa to evaluate our model on the public Automated Student Assessment Prize (ASAP) dataset, and the experimental results demonstrate that NPCR outperforms previous methods by a large margin, achieving the state-of-the-art average performance for the AES task.</abstract>
       <url hash="75b73314">2022.coling-1.240</url>
       <bibkey>xie-etal-2022-automated</bibkey>
+      <pwccode url="https://github.com/carryckw/aes-npcr" additional="false">carryckw/aes-npcr</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/asap">ASAP-AES</pwcdataset>
     </paper>
     <paper id="241">
       <title>Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision</title>

data/xml/2023.sigdial.xml

@@ -554,6 +554,8 @@
       <url hash="635628f9">2023.sigdial-1.43</url>
       <bibkey>willemsen-etal-2023-resolving</bibkey>
       <doi>10.18653/v1/2023.sigdial-1.43</doi>
+      <pwccode url="https://github.com/willemsenbram/reference-resolution-via-text-generation" additional="false">willemsenbram/reference-resolution-via-text-generation</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/a-game-of-sorts">A Game Of Sorts</pwcdataset>
     </paper>
     <paper id="44">
       <title>Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning</title>

data/xml/2024.inlg.xml

@@ -422,6 +422,8 @@
       <abstract>We propose an approach to referring expression generation (REG) in visually grounded dialogue that is meant to produce referring expressions (REs) that are both discriminative and discourse-appropriate. Our method constitutes a two-stage process. First, we model REG as a text- and image-conditioned next-token prediction task. REs are autoregressively generated based on their preceding linguistic context and a visual representation of the referent. Second, we propose the use of discourse-aware comprehension guiding as part of a generate-and-rerank strategy through which candidate REs generated with our REG model are reranked based on their discourse-dependent discriminatory power. Results from our human evaluation indicate that our proposed two-stage approach is effective in producing discriminative REs, with higher performance in terms of text-image retrieval accuracy for reranked REs compared to those generated using greedy decoding.</abstract>
       <url hash="3f35bca8">2024.inlg-main.38</url>
       <bibkey>willemsen-skantze-2024-referring-expression</bibkey>
+      <pwccode url="https://github.com/willemsenbram/reg-with-guiding" additional="false">willemsenbram/reg-with-guiding</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/a-game-of-sorts">A Game Of Sorts</pwcdataset>
     </paper>
     <paper id="39">
       <title>The <fixed-case>G</fixed-case>ricean Maxims in <fixed-case>NLP</fixed-case> - A Survey</title>