EMNLP 2022main0 citations
End-to-End Neural Discourse Deixis Resolution in Dialogue
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
@inproceedings{li-ng-2022-end,
title = "End-to-End Neural Discourse Deixis Resolution in Dialogue",
author = "Li, Shengjie and
Ng, Vincent",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.778/",
doi = "10.18653/v1/2022.emnlp-main.778",
pages = "11322--11334"
}