# CAW-coref: Conjunction-Aware Word-level Coreference Resolution

arXiv: https://arxiv.org/abs/2310.06165 · HTML: https://mlsanity.com/paper/2310.06165/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 9 Oct 2023.

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

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (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)
- [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019)
- [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)
- [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://mlsanity.com/paper/2205.12644/) (2022)
- [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://mlsanity.com/paper/2209.04280/) (2022)
- [Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective](https://mlsanity.com/paper/2303.09092/) (2023)

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

- [WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Case](https://mlsanity.com/paper/2409.05653/) (2024)
- [CorPipe at CRAC 2024: Predicting Zero Mentions from Raw Text](https://mlsanity.com/paper/2410.02756/) (2024)
- [GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction](https://mlsanity.com/paper/2504.10792/) (2025)
- [Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?](https://mlsanity.com/paper/2509.17796/) (2025)
- [Exploring Multiple Strategies to Improve Multilingual Coreference Resolution in CorefUD](https://mlsanity.com/paper/2408.16893/) (2024)
- [CorPipe at CRAC 2025: Evaluating Multilingual Encoders for Multilingual Coreference Resolution](https://mlsanity.com/paper/2509.17858/) (2025)
- [Pantagruel: Unified Self-Supervised Encoders for French Text and Speech](https://mlsanity.com/paper/2601.05911/) (2026)
- [ThaiCoref: Thai Coreference Resolution Dataset](https://mlsanity.com/paper/2406.06000/) (2024)
- [CorefInst: Leveraging LLMs for Multilingual Coreference Resolution](https://mlsanity.com/paper/2509.17505/) (2025)
- [CorPipe at CRAC 2026: Empty Nodes and Cross-Lingual Transfer in Multilingual Coreference Resolution](https://mlsanity.com/paper/2605.30133/) (2026)

## Abstract (arXiv)

State-of-the-art coreference resolutions systems depend on multiple LLM calls per document and are thus prohibitively expensive for many use cases (e.g., information extraction with large corpora). The leading word-level coreference system (WL-coref) attains 96.6% of these SOTA systems' performance while being much more efficient. In this work, we identify a routine yet important failure case of WL-coref: dealing with conjoined mentions such as 'Tom and Mary'. We offer a simple yet effective solution that improves the performance on the OntoNotes test set by 0.9% F1, shrinking the gap between efficient word-level coreference resolution and expensive SOTA approaches by 34.6%. Our Conjunction-Aware Word-level coreference model (CAW-coref) and code is available at https://github.com/KarelDO/wl-coref.