# Adapted End-to-End Coreference Resolution System for Anaphoric Identities in Dialogues

arXiv: https://arxiv.org/abs/2109.00185 · HTML: https://mlsanity.com/paper/2109.00185/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [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)
- [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019)
- [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://mlsanity.com/paper/1606.01323/) (2016)
- [Revealing the Myth of Higher-Order Inference in Coreference Resolution](https://mlsanity.com/paper/2009.12013/) (2020)
- [DWIE: an entity-centric dataset for multi-task document-level information extraction](https://mlsanity.com/paper/2009.12626/) (2020)
- [A Cluster Ranking Model for Full Anaphora Resolution](https://mlsanity.com/paper/1911.09532/) (2019)

## Built on by (most cited first, 5 of 5)

- [Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy](https://mlsanity.com/paper/2402.12821/) (2024)
- [Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction](https://mlsanity.com/paper/2205.01909/) (2022)
- [Online Coreference Resolution for Dialogue Processing: Improving Mention-Linking on Real-Time Conversations](https://mlsanity.com/paper/2205.10670/) (2022)
- [Multilingual Coreference Resolution in Multiparty Dialogue](https://mlsanity.com/paper/2208.01307/) (2022)
- [Anaphora Resolution in Dialogue: System Description (CODI-CRAC 2022 Shared Task)](https://mlsanity.com/paper/2301.02113/) (2023)

## Abstract (arXiv)

We present an effective system adapted from the end-to-end neural coreference resolution model, targeting on the task of anaphora resolution in dialogues. Three aspects are specifically addressed in our approach, including the support of singletons, encoding speakers and turns throughout dialogue interactions, and knowledge transfer utilizing existing resources. Despite the simplicity of our adaptation strategies, they are shown to bring significant impact to the final performance, with up to 27 F1 improvement over the baseline. Our final system ranks the 1st place on the leaderboard of the anaphora resolution track in the CRAC 2021 shared task, and achieves the best evaluation results on all four datasets.