# Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization

arXiv: https://arxiv.org/abs/2309.11582 · HTML: https://mlsanity.com/paper/2309.11582/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [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)
- [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019)
- [Word-Level Coreference Resolution](https://mlsanity.com/paper/2109.04127/) (2021)
- [On Generalization in Coreference Resolution](https://mlsanity.com/paper/2109.09667/) (2021)
- [OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres](https://mlsanity.com/paper/2106.00933/) (2021)
- [Bridging Anaphora Resolution as Question Answering](https://mlsanity.com/paper/2004.07898/) (2020)
- [Lexical Features in Coreference Resolution: To be Used With Caution](https://mlsanity.com/paper/1704.06779/) (2017)
- [Improving Generalization in Coreference Resolution via Adversarial Training](https://mlsanity.com/paper/1908.04728/) (2019)
- [Multi-task Learning Based Neural Bridging Reference Resolution](https://mlsanity.com/paper/2003.03666/) (2020)
- [Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution](https://mlsanity.com/paper/2009.09363/) (2020)

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

- [SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution](https://mlsanity.com/paper/2403.17245/) (2024)

## Abstract (arXiv)

Previous attempts to incorporate a mention detection step into end-to-end neural coreference resolution for English have been hampered by the lack of singleton mention span data as well as other entity information. This paper presents a coreference model that learns singletons as well as features such as entity type and information status via a multi-task learning-based approach. This approach achieves new state-of-the-art scores on the OntoGUM benchmark (+2.7 points) and increases robustness on multiple out-of-domain datasets (+2.3 points on average), likely due to greater generalizability for mention detection and utilization of more data from singletons when compared to only coreferent mention pair matching.