Paraphrasing vs Coreferring: Two Sides of the Same Coin

Yehudit Meged, Avi Caciularu, Vered Shwartz, Ido Dagan


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
Venues:
EMNLP | 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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.440.pdf
Video:
 https://slideslive.com/38940701
Code
 yehudit96/coreferrability +  additional community code
Data
ECB+