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.
@article{arxiv.2004.14979,
title = {Paraphrasing vs Coreferring: Two Sides of the Same Coin},
author = {Yehudit Meged and Avi Caciularu and Vered Shwartz and Ido Dagan},
journal= {arXiv preprint arXiv:2004.14979},
year = {2020}
}