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        <title>Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation</title>
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    <abstract>Identifying semantically equivalent sentences is important for many NLP tasks. Current approaches to semantic equivalence take a loose, sentence-level approach to “equivalence,” despite evidence that fine-grained differences and implicit content have an effect on human understanding and system performance. In this work, we introduce a novel, more sensitive method of characterizing cross-lingual semantic equivalence that leverages Abstract Meaning Representation graph structures. We find that parsing sentences into AMRs and comparing the AMR graphs enables finer-grained equivalence measurement than comparing the sentences themselves. We demonstrate that when using gold or even automatically parsed AMR annotations, our solution is finer-grained than existing corpus filtering methods and more accurate at predicting strictly equivalent sentences than existing semantic similarity metrics.</abstract>
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        <url>https://aclanthology.org/2023.iwcs-1.16</url>
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%0 Conference Proceedings
%T Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation
%A Wein, Shira
%A Wang, Zhuxin
%A Schneider, Nathan
%Y Amblard, Maxime
%Y Breitholtz, Ellen
%S Proceedings of the 15th International Conference on Computational Semantics
%D 2023
%8 June
%I Association for Computational Linguistics
%C Nancy, France
%F wein-etal-2023-measuring
%X Identifying semantically equivalent sentences is important for many NLP tasks. Current approaches to semantic equivalence take a loose, sentence-level approach to “equivalence,” despite evidence that fine-grained differences and implicit content have an effect on human understanding and system performance. In this work, we introduce a novel, more sensitive method of characterizing cross-lingual semantic equivalence that leverages Abstract Meaning Representation graph structures. We find that parsing sentences into AMRs and comparing the AMR graphs enables finer-grained equivalence measurement than comparing the sentences themselves. We demonstrate that when using gold or even automatically parsed AMR annotations, our solution is finer-grained than existing corpus filtering methods and more accurate at predicting strictly equivalent sentences than existing semantic similarity metrics.
%U https://aclanthology.org/2023.iwcs-1.16
%P 144-154

Markdown (Informal)

[Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation](https://aclanthology.org/2023.iwcs-1.16) (Wein et al., IWCS 2023)

• Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation (Wein et al., IWCS 2023)

ACL

• Shira Wein, Zhuxin Wang, and Nathan Schneider. 2023. Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation. In Proceedings of the 15th International Conference on Computational Semantics, pages 144–154, Nancy, France. Association for Computational Linguistics.