Abstract
Paraphrase identification (PI) and natural language inference (NLI) are two important tasks in natural language processing. Despite their distinct objectives, an underlying connection exists, which has been notably under-explored in empirical investigations. We formalize the relationship between these semantic tasks and introduce a method for solving PI using an NLI system, including the adaptation of PI datasets for fine-tuning NLI models. Through extensive evaluations on six PI benchmarks, across both zero-shot and fine-tuned settings, we showcase the efficacy of NLI models for PI through our proposed reduction. Remarkably, our fine-tuning procedure enables NLI models to outperform dedicated PI models on PI datasets. In addition, our findings provide insights into the limitations of current PI benchmarks.- Anthology ID:
- 2024.starsem-1.11
- Volume:
- Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Danushka Bollegala, Vered Shwartz
- Venue:
- *SEM
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 133–141
- Language:
- URL:
- https://aclanthology.org/2024.starsem-1.11
- DOI:
- Cite (ACL):
- Ning Shi, Bradley Hauer, Jai Riley, and Grzegorz Kondrak. 2024. Paraphrase Identification via Textual Inference. In Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024), pages 133–141, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Paraphrase Identification via Textual Inference (Shi et al., *SEM 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.starsem-1.11.pdf