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End-to-End Neural Discourse Deixis Resolution in Dialogue

arXiv 2211.15980 ↗First posted 29 Nov 2022cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 32 references that are in the map. First posted on arXiv 29 Nov 2022.

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7 of 32 references.

References · 7

  1. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 834
  2. 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 209
  3. 2018Higher-order Coreference Resolution with Coarse-to-fine Inferencecited by 150
  4. 2019BERT for Coreference Resolution: Baselines and Analysiscited by 125
  5. 2019Learning to Speak and Act in a Fantasy Text Adventure Gamecited by 122
  6. 2020Revealing the Myth of Higher-Order Inference in Coreference Resolutioncited by 49
  7. 2017A Mention-Ranking Model for Abstract Anaphora Resolutioncited by 4

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  1. 2024Explaining Relationships Among Research Paperscited by 3