# End-to-End Neural Discourse Deixis Resolution in Dialogue

arXiv: https://arxiv.org/abs/2211.15980 · HTML: https://mlsanity.com/paper/2211.15980/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 7 of 7)

- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [Learning to Speak and Act in a Fantasy Text Adventure Game](https://mlsanity.com/paper/1903.03094/) (2019)
- [Revealing the Myth of Higher-Order Inference in Coreference Resolution](https://mlsanity.com/paper/2009.12013/) (2020)
- [A Mention-Ranking Model for Abstract Anaphora Resolution](https://mlsanity.com/paper/1706.02256/) (2017)

## Built on by (most cited first, 1 of 1)

- [Explaining Relationships Among Research Papers](https://mlsanity.com/paper/2402.13426/) (2024)

## 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.