# Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution

arXiv: https://arxiv.org/abs/2210.07602 · HTML: https://mlsanity.com/paper/2210.07602/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 40 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [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)
- [An Annotated Dataset of Coreference in English Literature](https://mlsanity.com/paper/1912.01140/) (2019)
- [Coreference Resolution without Span Representations](https://mlsanity.com/paper/2101.00434/) (2021)
- [Word-Level Coreference Resolution](https://mlsanity.com/paper/2109.04127/) (2021)
- [On Generalization in Coreference Resolution](https://mlsanity.com/paper/2109.09667/) (2021)
- [A Human-Centered Review of the Algorithms used within the U.S. Child Welfare System](https://mlsanity.com/paper/2003.03541/) (2020)
- [Moving on from OntoNotes: Coreference Resolution Model Transfer](https://mlsanity.com/paper/2104.08457/) (2021)
- [Adapting Coreference Resolution Models through Active Learning](https://mlsanity.com/paper/2104.07611/) (2021)
- [Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution](https://mlsanity.com/paper/2009.09363/) (2020)
- [Can we Fix the Scope for Coreference? Problems and Solutions for Benchmarks beyond OntoNotes](https://mlsanity.com/paper/2112.09742/) (2021)
- [Scoring Coreference Chains with Split-Antecedent Anaphors](https://mlsanity.com/paper/2205.12323/) (2022)
- [Active Testing: An Unbiased Evaluation Method for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2010.08777/) (2020)
- [Stay Together: A System for Single and Split-antecedent Anaphora Resolution](https://mlsanity.com/paper/2104.05320/) (2021)
- [Adapting Coreference Resolution for Processing Violent Death Narratives](https://mlsanity.com/paper/2104.14703/) (2021)
- [Anatomy of OntoGUM--Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms](https://mlsanity.com/paper/2110.05727/) (2021)
- [Model-based annotation of coreference](https://mlsanity.com/paper/1906.10724/) (2019)

## Built on by (most cited first, 6 of 6)

- [Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains](https://mlsanity.com/paper/2410.08327/) (2024)
- [Major Entity Identification: A Generalizable Alternative to Coreference Resolution](https://mlsanity.com/paper/2406.14654/) (2024)
- [SynthTextEval: Synthetic Text Data Generation and Evaluation for High-Stakes Domains](https://mlsanity.com/paper/2507.07229/) (2025)
- [Semi-supervised multimodal coreference resolution in image narrations](https://mlsanity.com/paper/2310.13619/) (2023)
- [Controlled Generation for Private Synthetic Text](https://mlsanity.com/paper/2509.25729/) (2025)
- [Task Decomposition for Efficient Annotation](https://mlsanity.com/paper/2606.24734/) (2026)

## Abstract (arXiv)

Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, transferring these models to new target domains containing out-of-vocabulary spans and requiring differing annotation schemes remains challenging. Typical approaches involve continued training on annotated target-domain data, but obtaining annotations is costly and time-consuming. We show that annotating mentions alone is nearly twice as fast as annotating full coreference chains. Accordingly, we propose a method for efficiently adapting coreference models, which includes a high-precision mention detection objective and requires annotating only mentions in the target domain. Extensive evaluation across three English coreference datasets: CoNLL-2012 (news/conversation), i2b2/VA (medical notes), and previously unstudied child welfare notes, reveals that our approach facilitates annotation-efficient transfer and results in a 7-14% improvement in average F1 without increasing annotator time.