--- title: "BERT for Coreference Resolution — Joshi 2019" description: "This paper tests how much a pretrained BERT encoder helps coreference resolution." authors: ["Mandar Joshi", "Omer Levy", "Luke Zettlemoyer", "Daniel Weld"] year: 2019 venue: "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)" source: "https://aclanthology.org/D19-1588/" doi: "10.18653/v1/d19-1588" arxiv: "1908.09091" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/joshi-2019-bert-coreference-resolution-baselines-analysis/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/joshi-2019-bert-coreference-resolution-baselines-analysis/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2019](https://md.research.elamur.ai/free/en/papers/2019/) / BERT for Coreference Resolution: Baselines and Analysis # BERT for Coreference Resolution: Baselines and Analysis **Authors:** [Mandar Joshi](https://md.research.elamur.ai/free/en/authors/mandar-joshi/), [Omer Levy](https://md.research.elamur.ai/free/en/authors/omer-levy/), [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/), [Daniel Weld](https://md.research.elamur.ai/free/en/authors/daniel-weld/) · **Year:** 2019 · **Venue:** Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/D19-1588/) · [PDF](https://aclanthology.org/D19-1588.pdf) · [DOI 10.18653/v1/d19-1588](https://doi.org/10.18653/v1/d19-1588) · [arXiv 1908.09091](https://arxiv.org/abs/1908.09091) · [OpenAlex](https://openalex.org/W2969605360) **Citations:** 312 citations (openalex-snapshot · 2026-10-14) · [176](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary This paper tests how much a pretrained BERT encoder helps coreference resolution. It also looks at where the resulting system still fails. The authors start from c2f-coref (Lee et al., 2018), a span-ranking model, and swap its LSTM encoder, which used ELMo and GloVe inputs, for a fine-tuned BERT. Long documents have to be cut into segments. They try two ways of doing this: separate non-overlapping chunks ("independent"), and half-overlapping chunks whose token vectors are merged by a learned gate ("overlap"). Training uses English OntoNotes 5.0. The models are evaluated on OntoNotes and on the GAP pronoun dataset. BERT-large gives clear gains over the ELMo baseline on both, while BERT-base helps only slightly on OntoNotes. A manual error analysis shows that the large model is better at keeping similar but distinct entities apart. Long documents, pronouns in dialogue and paraphrased mentions remain hard, and longer context windows do not help. The study set a strong reference point for transformer-based coreference, and the code and models are public. ## Key points - **OntoNotes test (average F1 of MUC, B³, CEAF φ4):** c2f-coref scores 73.0, BERT-base 73.9 (+0.9) and BERT-large 76.9 (+3.9). The overlap variant gives no gain over independent. - **GAP (overall F1, trained only on OntoNotes):** c2f-coref scores 73.5, BERT-base 82.8 and BERT-large 85.0 (+11.5). - **Best segment length:** 128 word pieces for BERT-base and 384 for BERT-large. Both get worse at 450 and 512 (dev F1 falls to 70.7 and 73.6), and F1 drops steadily as documents get longer. - **Manual error count:** on 15 OntoNotes dev documents, BERT-base made 93 cluster-level errors and BERT-large made 74. The main error types are pronouns (especially in conversations), mention paraphrasing and missing world knowledge. - **Training setup:** separate learning rates for BERT (1e-5) and the task layers (2e-4) added 2–3 points. Training BERT-large needs 32GB GPUs. The code is at github.com/mandarjoshi90/coref. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) ## Cited by Cited by 176 papers of our library cite it (of the 140673 whose bibliographies we read); 55 of them have a page here, 121 not yet. - [Findings of the Second Shared Task on Multilingual Coreference Resolution](https://md.research.elamur.ai/free/en/papers/abokrtsky-2023-findings-second-shared-task-multilingual/) - [Neural Coreference Resolution for Arabic](https://md.research.elamur.ai/free/en/papers/aloraini-2020-neural-coreference-resolution-arabic/) - [Anaphora Resolution in Dialogue: Description of the DFKI-TalkingRobots System for the CODI-CRAC 2021 Shared-Task](https://md.research.elamur.ai/free/en/papers/anikina-2021-anaphora-resolution-dialogue-description-dfki/) - [Character Coreference Resolution in Movie Screenplays](https://md.research.elamur.ai/free/en/papers/baruah-2023-character-coreference-resolution-movie-screenplays/) - [Recognition of They/Them as Singular Personal Pronouns in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/baumler-2022-recognition-they-them-as-singular/) - [Coreference Resolution through a seq2seq Transition-Based System](https://md.research.elamur.ai/free/en/papers/bohnet-2023-coreference-resolution-through-seq2seq-transition/) - [Cross-document Coreference Resolution over Predicted Mentions](https://md.research.elamur.ai/free/en/papers/cattan-2021-cross-document-coreference-resolution-over/) - [Realistic Evaluation Principles for Cross-document Coreference Resolution](https://md.research.elamur.ai/free/en/papers/cattan-2021-realistic-evaluation-principles-cross-document/) - [Investigating Multilingual Coreference Resolution by Universal Annotations](https://md.research.elamur.ai/free/en/papers/chai-2023-investigating-multilingual-coreference-resolution-universal/) - [Cross-Document Event Coreference Resolution on Discourse Structure](https://md.research.elamur.ai/free/en/papers/chen-2023-cross-document-event-coreference-resolution/) - [Neural End-to-end Coreference Resolution for German in Different Domains](https://md.research.elamur.ai/free/en/papers/coref--schroder-neural-end-end-coreference-resolution/) - [Do UD Trees Match Mention Spans in Coreference Annotations?](https://md.research.elamur.ai/free/en/papers/coref-2021-popel-do-ud-trees-match-mention/) - [Beyond Word Boundaries: A Hebrew Coreference Benchmark and an Evaluation Protocol for Morphologically Complex Text](https://md.research.elamur.ai/free/en/papers/coref-2026-greenfeld-beyond-word-boundaries-hebrew-coreference/) - [RobBERT: a Dutch RoBERTa-based Language Model](https://md.research.elamur.ai/free/en/papers/delobelle-2020-robbert-dutch-roberta-based-language/) - [WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia](https://md.research.elamur.ai/free/en/papers/eirew-2021-wec-deriving-large-scale-cross/) - [Cross-document Event Coreference Search: Task, Dataset and Modeling](https://md.research.elamur.ai/free/en/papers/eirew-2022-cross-document-event-coreference-search/) - [Dual Cache for Long Document Neural Coreference Resolution](https://md.research.elamur.ai/free/en/papers/guo-2023-dual-cache-long-document-neural/) - [Lions: 1 and Tigers: 2 and Bears: 3, Oh My! 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[AMALGUM – A Free, Balanced, Multilayer English Web Corpus](https://md.research.elamur.ai/free/en/papers/lingbulk-2020-gessler-amalgum-free-balanced-multilayer-english/) - [Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art](https://md.research.elamur.ai/free/en/papers/lu-2020-conundrums-entity-coreference-resolution-making/) - [Constrained Multi-Task Learning for Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/lu-2021-constrained-multi-task-learning-event/) - [Conundrums in Event Coreference Resolution: Making Sense of the State of the Art](https://md.research.elamur.ai/free/en/papers/lu-2021-conundrums-event-coreference-resolution-making/) - [Coreference Resolution for the Biomedical Domain: A Survey](https://md.research.elamur.ai/free/en/papers/lu-2021-coreference-resolution-biomedical-domain-survey/) - [Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends](https://md.research.elamur.ai/free/en/papers/martinelli-2024-maverick-efficient-accurate-coreference-resolution/) - [Exploring Pre-Trained Transformers and Bilingual Transfer Learning for Arabic Coreference Resolution](https://md.research.elamur.ai/free/en/papers/min-2021-exploring-pre-trained-transformers-bilingual/) - [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/) - [Multilingual Coreference Resolution in Low-resource South Asian Languages](https://md.research.elamur.ai/free/en/papers/mishra-2024-multilingual-coreference-resolution-low-resource/) - [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://md.research.elamur.ai/free/en/papers/otmazgin-2022-f-coref-fast-accurate-easy/) - [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/otmazgin-2023-lingmess-linguistically-informed-multi-expert/) - [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/) - [A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News](https://md.research.elamur.ai/free/en/papers/poot-2020-benchmark-rule-based-neural-coreference/) - [A Controlled Reevaluation of Coreference Resolution Models](https://md.research.elamur.ai/free/en/papers/porada-2024-controlled-reevaluation-coreference-resolution-models/) - [Multilingual Coreference Resolution with Harmonized Annotations](https://md.research.elamur.ai/free/en/papers/prazak-2021-multilingual-coreference-resolution-harmonized-annotations/) - [Scaling Within Document Coreference to Long Texts](https://md.research.elamur.ai/free/en/papers/thirukovalluru-2021-scaling-within-document-coreference-long/) - [Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks](https://md.research.elamur.ai/free/en/papers/toshniwal-2020-learning-ignore-long-document-coreference/) - [Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/tran-2021-exploiting-document-structures-cluster-consistencies/) - 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Problems and Solutions for Benchmarks beyond OntoNotes](https://md.research.elamur.ai/free/en/papers/zeldes-2022-opinion-piece-can-we-fix/) - [Seq2seq is All You Need for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/zhang-2023-seq2seq-is-all-you-need/) - [Anatomy of OntoGUM—Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms](https://md.research.elamur.ai/free/en/papers/zhu-2021-anatomy-ontogum-adapting-gum-ontonotes/) - [OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres](https://md.research.elamur.ai/free/en/papers/zhu-2021-ontogum-evaluating-contextualized-sota-coreference/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{joshi2019bert, title = {{BERT for Coreference Resolution: Baselines and Analysis}}, author = {Joshi, Mandar and Levy, Omer and Zettlemoyer, Luke and Weld, Daniel}, booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, year = {2019}, doi = {10.18653/v1/d19-1588}, eprint = {1908.09091}, archivePrefix = {arXiv}, url = {https://doi.org/10.18653/v1/d19-1588} } ``` - [← previous Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://md.research.elamur.ai/free/en/papers/yin-2019-benchmarking-zero-shot-text-classification/) - [next → BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model](https://md.research.elamur.ai/free/en/papers/wang-2019-bert-has-mouth-must-speak/) - [↑ back to papers of 2019](https://md.research.elamur.ai/free/en/papers/2019/) ## References 22 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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