Joint Learning for Event Coreference Resolution

Jing Lu and Vincent Ng.
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 90-101, 2017.

Click here for the PDF version. The talk slides are available here.


While joint models have been developed for many NLP tasks, the vast majority of event coreference resolvers, including the top-performing resolvers competing in the recent TAC KBP 2016 Event Nugget Detection and Coreference task, are pipelinebased, where the propagation of errors from the trigger detection component to the event coreference component is a major performance limiting factor. To address this problem, we propose a model for jointly learning event coreference, trigger detection, and event anaphoricity. Our joint model is novel in its choice of tasks and its features for capturing cross-task interactions. To our knowledge, this is the first attempt to train a mention-ranking model and employ event anaphoricity for event coreference. Our model achieves the best results to date on the KBP 2016 English and Chinese datasets.

BibTeX entry

  author = {Jing Lu and Vincent Ng},
  title = {Joint Learning for Event Coreference Resolution},
  booktitle = {Proceedings of 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages = {90--101}, 
  year = 2017}