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2024.textgraphs-1.3: title, abstract spaces #3950

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Oct 12, 2024
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2024.textgraphs-1.3: title, abstract spaces #3950

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4 changes: 2 additions & 2 deletions data/xml/2024.textgraphs.xml
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Expand Up @@ -51,11 +51,11 @@
<bibkey>brannon-etal-2024-congrat</bibkey>
</paper>
<paper id="3">
<title>A Pipeline Approach for Parsing Documents into Uniform Meaning Representation Graphs</title>
<title>Uniform Meaning Representation Parsing as a Pipelined Approach</title>
<author><first>Jayeol</first><last>Chun</last><affiliation>Brandeis University</affiliation></author>
<author><first>Nianwen</first><last>Xue</last><affiliation>Brandeis University</affiliation></author>
<pages>40-52</pages>
<abstract>Uniform Meaning Representation (UMR) is the next phase of semantic formalism following Abstract Meaning Representation (AMR), with added focus on inter-sentential relations allowing the representational scope of UMR to cover a full document.This, in turn, greatly increases the complexity of its parsing task with the additional requirement of capturing document-level linguistic phenomena such as coreference, modal and temporal dependencies.In order to establish a strong baseline despite the small size of recently released UMR v1.0 corpus, we introduce a pipeline model that does not require any training.At the core of our method is a two-track strategy of obtaining UMR’s sentence and document graphs separately, with the document-level triples being compiled at the token level and the sentence graph being converted from AMR graphs.By leveraging alignment between AMR and its sentence, we are able to generate the first automatic English UMR parses.</abstract>
<abstract>Uniform Meaning Representation (UMR) is the next phase of semantic formalism following Abstract Meaning Representation (AMR), with added focus on inter-sentential relations allowing the representational scope of UMR to cover a full document. This, in turn, greatly increases the complexity of its parsing task with the additional requirement of capturing document-level linguistic phenomena such as coreference, modal and temporal dependencies. In order to establish a strong baseline despite the small size of recently released UMR v1.0 corpus, we introduce a pipeline model that does not require any training. At the core of our method is a two-track strategy of obtaining UMR’s sentence and document graphs separately, with the document-level triples being compiled at the token level and the sentence graph being converted from AMR graphs. By leveraging alignment between AMR and its sentence, we are able to generate the first automatic English UMR parses.</abstract>
<url hash="bdc62095">2024.textgraphs-1.3</url>
<bibkey>chun-xue-2024-pipeline</bibkey>
</paper>
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