End-to-End Neural Discourse Deixis Resolution in Dialogue

Shengjie Li and Vincent Ng.
Proceedings of the 2022 Empirical Methods in Natural Language Processing, pp. 11322-11334, 2022.

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Abstract

We adapt Lee et al.'s (2018) span-based entity coreference model to the task of end-to-end discourse deixis resolution in dialogue, specifically by proposing extensions to their model that exploit task-specific characteristics. The resulting model, dd-utt, achieves state-of-the-art results on the four datasets in the CODI-CRAC 2021 shared task.

BibTeX entry

@InProceedings{Li+Ng:22a,
  author = {Shengjie Li and Vincent Ng},
  title = {End-to-End Neural Discourse Deixis Resolution in Dialogue},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  pages = {11322--11334}, 
  year = 2022}