# Seq2seq is All You Need for Coreference Resolution

arXiv: https://arxiv.org/abs/2310.13774 · HTML: https://mlsanity.com/paper/2310.13774/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 37 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [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)
- [An Annotated Dataset of Coreference in English Literature](https://mlsanity.com/paper/1912.01140/) (2019)
- [Revealing the Myth of Higher-Order Inference in Coreference Resolution](https://mlsanity.com/paper/2009.12013/) (2020)
- [Coreference Resolution without Span Representations](https://mlsanity.com/paper/2101.00434/) (2021)
- [Autoregressive Structured Prediction with Language Models](https://mlsanity.com/paper/2210.14698/) (2022)
- [Word-Level Coreference Resolution](https://mlsanity.com/paper/2109.04127/) (2021)
- [Coreference Resolution through a seq2seq Transition-Based System](https://mlsanity.com/paper/2211.12142/) (2022)
- [PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution](https://mlsanity.com/paper/1810.09807/) (2018)
- [Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks](https://mlsanity.com/paper/2010.02807/) (2020)
- [On Generalization in Coreference Resolution](https://mlsanity.com/paper/2109.09667/) (2021)
- [Moving on from OntoNotes: Coreference Resolution Model Transfer](https://mlsanity.com/paper/2104.08457/) (2021)
- [Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution](https://mlsanity.com/paper/2009.09363/) (2020)
- [A Sequence-to-Sequence Model for Semantic Role Labeling](https://mlsanity.com/paper/1807.03006/) (2018)

## Built on by (most cited first, 10 of 10)

- [Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding](https://mlsanity.com/paper/2410.01671/) (2024)
- [[Lions: 1] and [Tigers: 2] and [Bears: 3], Oh My! Literary Coreference Annotation with LLMs](https://mlsanity.com/paper/2401.17922/) (2024)
- [Post-training for Efficient Communication via Convention Formation](https://mlsanity.com/paper/2508.06482/) (2025)
- [Multimodal Coreference Resolution for Chinese Social Media Dialogues: Dataset and Benchmark Approach](https://mlsanity.com/paper/2504.14321/) (2025)
- [Improving LLMs' Learning for Coreference Resolution](https://mlsanity.com/paper/2509.11466/) (2025)
- [BoundRL: Efficient Structured Text Segmentation through Reinforced Boundary Generation](https://mlsanity.com/paper/2510.20151/) (2025)
- [End-to-End Dialog Neural Coreference Resolution: Balancing Efficiency and Accuracy in Large-Scale Systems](https://mlsanity.com/paper/2504.05824/) (2025)
- [CorefInst: Leveraging LLMs for Multilingual Coreference Resolution](https://mlsanity.com/paper/2509.17505/) (2025)
- [Efficient Seq2seq Coreference Resolution Using Entity Representations](https://mlsanity.com/paper/2510.14504/) (2025)
- [Closing the Gap at CRAC 2026: Two-Stage Adaptation for LLM-Based Multilingual Coreference Resolution](https://mlsanity.com/paper/2605.16984/) (2026)

## Abstract (arXiv)

Existing works on coreference resolution suggest that task-specific models are necessary to achieve state-of-the-art performance. In this work, we present compelling evidence that such models are not necessary. We finetune a pretrained seq2seq transformer to map an input document to a tagged sequence encoding the coreference annotation. Despite the extreme simplicity, our model outperforms or closely matches the best coreference systems in the literature on an array of datasets. We also propose an especially simple seq2seq approach that generates only tagged spans rather than the spans interleaved with the original text. Our analysis shows that the model size, the amount of supervision, and the choice of sequence representations are key factors in performance.