Paraphrasing vs Coreferring: Two Sides of the Same Coin
TL
TL;DR
This work used annotations from an event coreference dataset as distant supervision to re-score heuristically-extracted predicate paraphrases, and used the same re-ranking features as additional inputs to a state-of-the-art eventcoreference resolution model, which yielded modest but consistent improvements to the model’s performance.
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.
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