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        <title>End-to-End Neural Discourse Deixis Resolution in Dialogue</title>
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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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.778</identifier>
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%0 Conference Proceedings
%T End-to-End Neural Discourse Deixis Resolution in Dialogue
%A Li, Shengjie
%A Ng, Vincent
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-ng-2022-end
%X 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.
%R 10.18653/v1/2022.emnlp-main.778
%U https://aclanthology.org/2022.emnlp-main.778
%U https://doi.org/10.18653/v1/2022.emnlp-main.778
%P 11322-11334

Markdown (Informal)

[End-to-End Neural Discourse Deixis Resolution in Dialogue](https://aclanthology.org/2022.emnlp-main.778) (Li & Ng, EMNLP 2022)

• End-to-End Neural Discourse Deixis Resolution in Dialogue (Li & Ng, EMNLP 2022)

ACL

• Shengjie Li and Vincent Ng. 2022. End-to-End Neural Discourse Deixis Resolution in Dialogue. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11322–11334, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.