Abstract
We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution. First, we used annotations from an event coreference dataset as distant supervision to re-score heuristically-extracted predicate paraphrases. The new scoring gained more than 18 points in average precision upon their ranking by the original scoring method. Then, we used the same re-ranking features as additional inputs to a state-of-the-art event coreference resolution model, which yielded modest but consistent improvements to the model’s performance. The results suggest a promising direction to leverage data and models for each of the tasks to the benefit of the other.- Anthology ID:
- 2020.findings-emnlp.440
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4897–4907
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.440
- DOI:
- 10.18653/v1/2020.findings-emnlp.440
- Cite (ACL):
- Yehudit Meged, Avi Caciularu, Vered Shwartz, and Ido Dagan. 2020. Paraphrasing vs Coreferring: Two Sides of the Same Coin. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4897–4907, Online. Association for Computational Linguistics.
- Cite (Informal):
- Paraphrasing vs Coreferring: Two Sides of the Same Coin (Meged et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.440.pdf
- Code
- yehudit96/coreferrability + additional community code
- Data
- ECB+