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.- Anthology ID:
- 2023.iwcs-1.16
- Volume:
- Proceedings of the 15th International Conference on Computational Semantics
- Month:
- June
- Year:
- 2023
- Address:
- Nancy, France
- Editors:
- Maxime Amblard, Ellen Breitholtz
- Venue:
- IWCS
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 144–154
- Language:
- URL:
- https://aclanthology.org/2023.iwcs-1.16
- DOI:
- Cite (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.
- Cite (Informal):
- Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation (Wein et al., IWCS 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.iwcs-1.16.pdf