Work

Paraphrasing vs Coreferring: Two Sides of the Same Coin

Year: 2020

Type: conference-paper

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.

DOI: 10.18653/v1/2020.findings-emnlp.440

Authors: Yehudit Meged, Avi Caciularu, Vered Shwartz, Ido Dagan

Institutions: Bar-Ilan University, Allen Institute for Artificial Intelligence, University of Washington

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: Deutsche Forschungsgemeinschaft, Israel Science Foundation

Award: G7805275943

OpenAlex ID: W3101528337