---
title: "Assessing the Capabilities of Large Language — Gan 2024"
description: "The authors ask how well modern LLMs handle coreference and what this means for coreference research."
authors: ["Yujian Gan", "Massimo Poesio", "Juntao Yu"]
year: 2024
venue: "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)"
source: "https://aclanthology.org/2024.lrec-main.145/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/gan-2024-assessing-capabilities-large-language-models/"
---
[Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/gan-2024-assessing-capabilities-large-language-models/)

[home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2024](https://md.research.elamur.ai/free/papers/2024/) / Assessing the Capabilities of Large Language Models in Coreference: An Evaluation

# Assessing the Capabilities of Large Language Models in Coreference: An Evaluation

**Authors:** Yujian Gan, [Massimo Poesio](https://md.research.elamur.ai/free/authors/massimo-poesio/), [Juntao Yu](https://md.research.elamur.ai/free/authors/juntao-yu/) · **Year:** 2024 · **Venue:** Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.lrec-main.145/) · [PDF](https://aclanthology.org/2024.lrec-main.145.pdf) · [OpenAlex](https://openalex.org/W7131799499)

**Citations:** 3 citations (openalex-snapshot · 2026-10-14) · [21](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary

The authors ask how well modern LLMs handle coreference and what this means for coreference research. They tested the GPT series (GPT3.5, GPT4, version 0613, temperature 0), the LLAMA2 family and Bard on two settings. The first was whole-document anaphora resolution on documents from the CRAC 2018 and CODI-CRAC 2021/2022 shared tasks: Penn Treebank and TRAINS from the ARRAU corpus, plus LIGHT dialogue. The second was a 150-item subset of WinoGrande.

Automatic scoring used few-shot Chain-of-Thought prompts with gold mentions, because the models could not reliably find exact mention boundaries. Under these conditions, every LLM fell well short of a supervised state-of-the-art baseline. When the models instead explained coreference in free-form text and humans judged the answers, scores rose sharply, and some exceeded the baseline.

The authors conclude that LLMs grasp coreference well, but current task formats and metrics do not capture this ability. They call for new datasets, annotations and evaluation methods, and for work on the fine-grained comprehension weaknesses that the models share.

## Key points

- **WinoGrande accuracy:** GPT4 reached 94%, GPT3.5 68% and LLAMA2-Chat 70B 57%. On 44 newly written items, built to rule out training-data exposure, GPT4 still scored 93.2%.
- **Automatic CRACs evaluation (UA score):**
  - GPT4 scored 67.68, 58.67 and 54.42 on LIGHT, Penn Treebank and TRAINS.
  - The baseline (Yu et al., 2020) scored 75.62, 83.90 and 65.82.
  - Fine-tuned LLAMA2 7B and 13B outperformed GPT4 and LLAMA2 70B but still trailed the baseline.
- **Manual evaluation:**
  - Two annotators with different strictness (A1 strict, A2 lenient) judged GPT4's natural-language analyses.
  - GPT4 reached UA scores of 73.51–77.59 on LIGHT and 68.24–75.05 on TRAINS, above the baseline on both, but stayed below it on Penn Treebank (75.90–78.35).
  - LLAMA2-Chat 70B scored lower than GPT4 under both annotators.
- **Mention detection:** in predicted-mention mode, the LLMs struggled to match the datasets' mention boundaries, especially for complex mentions, so evaluation switched to gold mentions.
- **Proposed directions:**
  - Broaden the scope of coreference tasks and score free-text analyses directly.
  - Annotate analysis descriptions rather than only coreference chains.
  - Investigate why different LLMs fail in similar ways on fine-grained understanding.

*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/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/) · [Treebanks](https://md.research.elamur.ai/free/topics/treebanks/)

## Cited by

Cited by

21 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 19 not yet.

- [CorefInst: Leveraging LLMs for Multilingual Coreference Resolution](https://md.research.elamur.ai/free/papers/arslan-2026-corefinst-leveraging-llms-multilingual-coreference/)
- [Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?](https://md.research.elamur.ai/free/papers/novak-2025-findings-fourth-shared-task-multilingual/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{gan2024assessing,
  title = {{Assessing the Capabilities of Large Language Models in Coreference: An Evaluation}},
  author = {Gan, Yujian and Poesio, Massimo and Yu, Juntao},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year = {2024},
  doi = {10.63317/3bxow5zxwb2a},
  url = {https://doi.org/10.63317/3bxow5zxwb2a}
}
```

- [← previous Are We on the Right Way for Evaluating Large Vision-Language Models?](https://md.research.elamur.ai/free/papers/chen-2024-right-way-evaluating-large-vision/)
- [next → Assessing the Cross-linguistic Utility of Abstract Meaning Representation](https://md.research.elamur.ai/free/papers/wein-2024-assessing-cross-linguistic-utility-abstract/)
- [↑ back to papers of 2024](https://md.research.elamur.ai/free/papers/2024/)

## References

33 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared… [find](https://scholar.google.com/scholar?q=Yuntao+Bai%2C+Saurav+Kadavath%2C+Sandipan+Kundu%2C+Amanda+Askell%2C+Jackson+Kernion%2C+Andy+Jones%2C+Anna+Chen%2C+Anna+Goldie%2C+Azalia+Mirhoseini%2C+Cameron+McKinnon%2C+Carol+Chen%2C+Catherine+Olsson%2C+Christopher+Olah%2C+Da) [our card](https://md.research.elamur.ai/free/papers/bai-2022-constitutional-ai-harmlessness-ai-feedback/)
2. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter,… [find](https://scholar.google.com/scholar?q=Tom+B%2E+Brown%2C+Benjamin+Mann%2C+Nick+Ryder%2C+Melanie+Subbiah%2C+Jared+Kaplan%2C+Prafulla+Dhariwal%2C+Arvind+Neelakantan%2C+Pranav+Shyam%2C+Girish+Sastry%2C+Amanda+Askell%2C+Sandhini+Agarwal%2C+Ariel+Herbert%2DVoss%2C+Gretche)
3. Jialun Cao, Yaojie Lu, Ming Wen, and Shing-Chi Cheung. 2023. Testing coreference resolution systems without labeled test sets. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ES-EC/FSE 2023, page 107–119, New Yor… [find](https://scholar.google.com/scholar?q=Jialun+Cao%2C+Yaojie+Lu%2C+Ming+Wen%2C+and+Shing%2DChi+Cheung%2E+2023%2E+Testing+coreference+resolution+systems+without+labeled+test+sets%2E+In+Proceedings+of+the+31st+ACM+Joint+European+Software+Engineering+Confer)
4. Cheng-Han Chiang and Hung yi Lee. 2023. A closer look into automatic evaluation using large language models. [find](https://scholar.google.com/scholar?q=Cheng%2DHan+Chiang+and+Hung+yi+Lee%2E+2023%2E+A+closer+look+into+automatic+evaluation+using+large+language+models%2E)
5. Carola Eschenbach, Christopher Habel, Michael Herweg, and Klaus Rehkämper. 1989. Remarks on plural anaphora. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Carola+Eschenbach%2C+Christopher+Habel%2C+Michael+Herweg%2C+and+Klaus+Rehk%C3%A4mper%2E+1989%2E+Remarks+on+plural+anaphora%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/papers/eschenbach-1989-remarks-plural-anaphora/)
6. Rebecca M. M. Hicke and David Mimno. 2024. [lions: 1] and [tigers: 2] and [bears: 3], oh my! literary coreference annotation with llms. [find](https://scholar.google.com/scholar?q=Rebecca+M%2E+M%2E+Hicke+and+David+Mimno%2E+2024%2E+%5Blions%3A+1%5D+and+%5Btigers%3A+2%5D+and+%5Bbears%3A+3%5D%2C+oh+my%21+literary+coreference+annotation+with+llms%2E) [our card](https://md.research.elamur.ai/free/papers/hicke-2024-lions-1-tigers-2-bears/)
7. Xue Jiang, Yihong Dong, Lecheng Wang, Qiwei Shang, and Ge Li. 2023. Self-planning code generation with large language model. [find](https://scholar.google.com/scholar?q=Xue+Jiang%2C+Yihong+Dong%2C+Lecheng+Wang%2C+Qiwei+Shang%2C+and+Ge+Li%2E+2023%2E+Self%2Dplanning+code+generation+with+large+language+model%2E)
8. Hans Kamp and Uwe Reyle. 1993. From Discourse to Logic. D. Reidel, Dordrecht. [find](https://scholar.google.com/scholar?q=Hans+Kamp+and+Uwe+Reyle%2E+1993%2E+From+Discourse+to+Logic%2E+D%2E+Reidel%2C+Dordrecht%2E)
9. Varada Kolhatkar, Adam Roussel, Stefanie Dipper, and Heike Zinsmeister. 2018. Anaphora with non-nominal antecedents in computational linguistics: a Survey. Computational Linguistics, 44(3):547– 612. [find](https://scholar.google.com/scholar?q=Varada+Kolhatkar%2C+Adam+Roussel%2C+Stefanie+Dipper%2C+and+Heike+Zinsmeister%2E+2018%2E+Anaphora+with+non%2Dnominal+antecedents+in+computational+linguistics%3A+a+Survey%2E+Computational+Linguistics%2C+44%283%29%3A547%E2%80%93+612%2E) [our card](https://md.research.elamur.ai/free/papers/kolhatkar-2018-survey-anaphora-non-nominal-antecedents/)
10. Nghia T. Le and Alan Ritter. 2023. Are large language models robust coreference resolvers? [find](https://scholar.google.com/scholar?q=Nghia+T%2E+Le+and+Alan+Ritter%2E+2023%2E+Are+large+language+models+robust+coreference+resolvers%3F) [our card](https://md.research.elamur.ai/free/papers/le-2023-large-language-models-robust-coreference/)
11. Hector J. Levesque, Ernest Davis, and Leona Morgenstern. 2012. The Winograd Schema Challenge. In Proc. of the AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning. [find](https://scholar.google.com/scholar?q=Hector+J%2E+Levesque%2C+Ernest+Davis%2C+and+Leona+Morgenstern%2E+2012%2E+The+Winograd+Schema+Challenge%2E+In+Proc%2E+of+the+AAAI+Spring+Symposium+on+Logical+Formalizations+of+Commonsense+Reasoning%2E)
12. Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022. Rethinking the role of demonstrations: What makes in-context learning work? [find](https://scholar.google.com/scholar?q=Sewon+Min%2C+Xinxi+Lyu%2C+Ari+Holtzman%2C+Mikel+Artetxe%2C+Mike+Lewis%2C+Hannaneh+Hajishirzi%2C+and+Luke+Zettlemoyer%2E+2022%2E+Rethinking+the+role+of+demonstrations%3A+What+makes+in%2Dcontext+learning+work%3F) [our card](https://md.research.elamur.ai/free/papers/min-2022-rethinking-role-demonstrations-what-makes/)
13. Dhruv Mullick, Bilal Ghanem, and Alona Fyshe. 2023. Better handling coreference resolution in aspect level sentiment classification by fine-tuning language models. [find](https://scholar.google.com/scholar?q=Dhruv+Mullick%2C+Bilal+Ghanem%2C+and+Alona+Fyshe%2E+2023%2E+Better+handling+coreference+resolution+in+aspect+level+sentiment+classification+by+fine%2Dtuning+language+models%2E)
14. Graziella Orrù, Andrea Piarulli, Ciro Conversano, and Angelo Gemignani. 2023. Human-like problem-solving abilities in large language models using chatgpt. Frontiers in Artificial Intelligence, 6. [find](https://scholar.google.com/scholar?q=Graziella+Orr%C3%B9%2C+Andrea+Piarulli%2C+Ciro+Conversano%2C+and+Angelo+Gemignani%2E+2023%2E+Human%2Dlike+problem%2Dsolving+abilities+in+large+language+models+using+chatgpt%2E+Frontiers+in+Artificial+Intelligence%2C+6%2E)
15. Silviu Paun, Juntao Yu, Nafise Sadat Moosavi, and Massimo Poesio. 2022. Scoring coreference chains with split-antecedent anaphors. [find](https://scholar.google.com/scholar?q=Silviu+Paun%2C+Juntao+Yu%2C+Nafise+Sadat+Moosavi%2C+and+Massimo+Poesio%2E+2022%2E+Scoring+coreference+chains+with+split%2Dantecedent+anaphors%2E) [our card](https://md.research.elamur.ai/free/papers/paun-2023-scoring-coreference-chains-split-antecedent/)
16. Massimo Poesio, Juntao Yu, Silviu Paun, Abdulrahman Aloraini, Pengcheng Lu, Janosch Haber, and Derya Cokal. 2023. Computational models of anaphora. Annual Review of Linguistics, 9:561–587. [find](https://scholar.google.com/scholar?q=Massimo+Poesio%2C+Juntao+Yu%2C+Silviu+Paun%2C+Abdulrahman+Aloraini%2C+Pengcheng+Lu%2C+Janosch+Haber%2C+and+Derya+Cokal%2E+2023%2E+Computational+models+of+anaphora%2E+Annual+Review+of+Linguistics%2C+9%3A561%E2%80%93587%2E)
17. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2023. Exploring the limits of transfer learning with a unified text-to-text transformer. [find](https://scholar.google.com/scholar?q=Colin+Raffel%2C+Noam+Shazeer%2C+Adam+Roberts%2C+Katherine+Lee%2C+Sharan+Narang%2C+Michael+Matena%2C+Yanqi+Zhou%2C+Wei+Li%2C+and+Peter+J%2E+Liu%2E+2023%2E+Exploring+the+limits+of+transfer+learning+with+a+unified+text%2Dto%2Dtex)
18. Altaf Rahman and Vincent Ng. 2012. Resolving complex cases of definite pronouns: the Winograd Schema Challenge. In Proc. of EMNLP/-CONLL, pages 777–789. [find](https://scholar.google.com/scholar?q=Altaf+Rahman+and+Vincent+Ng%2E+2012%2E+Resolving+complex+cases+of+definite+pronouns%3A+the+Winograd+Schema+Challenge%2E+In+Proc%2E+of+EMNLP%2F%2DCONLL%2C+pages+777%E2%80%93789%2E) [our card](https://md.research.elamur.ai/free/papers/rahman-2012-resolving-complex-cases-definite-pronouns/)
19. Natalia Skachkova, Tatiana Anikina, and Anna Mokhova. 2023. Multilingual coreference resolution: Adapt and generate. In Proceedings of the CRAC 2023 Shared Task on Multilingual Coreference Resolution, pages 19–33, Singapore. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Natalia+Skachkova%2C+Tatiana+Anikina%2C+and+Anna+Mokhova%2E+2023%2E+Multilingual+coreference+resolution%3A+Adapt+and+generate%2E+In+Proceedings+of+the+CRAC+2023+Shared+Task+on+Multilingual+Coreference+Resolution%2C)
20. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,… [find](https://scholar.google.com/scholar?q=Hugo+Touvron%2C+Louis+Martin%2C+Kevin+Stone%2C+Peter+Albert%2C+Amjad+Almahairi%2C+Yasmine+Babaei%2C+Nikolay+Bashlykov%2C+Soumya+Batra%2C+Prajjwal+Bhargava%2C+Shruti+Bhosale%2C+Dan+Bikel%2C+Lukas+Blecher%2C+Cristian+Canton+Fe)
21. Olga Uryupina, Ron Artstein, Antonella Bristot, Federica Cavicchio, Francesca Delogu, Kepa Rodriguez, and Massimo Poesio. 2020. Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU corpus. Journal of Natural Language Engineering, 26(1). [find](https://scholar.google.com/scholar?q=Olga+Uryupina%2C+Ron+Artstein%2C+Antonella+Bristot%2C+Federica+Cavicchio%2C+Francesca+Delogu%2C+Kepa+Rodriguez%2C+and+Massimo+Poesio%2E+2020%2E+Annotating+a+broad+range+of+anaphoric+phenomena%2C+in+a+variety+of+genres%3A)
22. Bonnie Lynn Webber. 1991. Structure and ostension in the interpretation of discourse deixis. Language and Cognitive Processes, 6(2):107–135. [find](https://scholar.google.com/scholar?q=Bonnie+Lynn+Webber%2E+1991%2E+Structure+and+ostension+in+the+interpretation+of+discourse+deixis%2E+Language+and+Cognitive+Processes%2C+6%282%29%3A107%E2%80%93135%2E)
23. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023. Chain-of-thought prompting elicits reasoning in large language models. [find](https://scholar.google.com/scholar?q=Jason+Wei%2C+Xuezhi+Wang%2C+Dale+Schuurmans%2C+Maarten+Bosma%2C+Brian+Ichter%2C+Fei+Xia%2C+Ed+Chi%2C+Quoc+Le%2C+and+Denny+Zhou%2E+2023%2E+Chain%2Dof%2Dthought+prompting+elicits+reasoning+in+large+language+models%2E) [our card](https://md.research.elamur.ai/free/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
24. Keith Tyser, Zad Chin, Yann Hicke, Nikhil Singh, Madeleine Udell, Yoon Kim, Tonio Buonassisi, Armando Solar-Lezama, and Iddo Drori. 2023. Exploring the mit mathematics and eecs curriculum using large language models. 11. [find](https://scholar.google.com/scholar?q=Keith+Tyser%2C+Zad+Chin%2C+Yann+Hicke%2C+Nikhil+Singh%2C+Madeleine+Udell%2C+Yoon+Kim%2C+Tonio+Buonassisi%2C+Armando+Solar%2DLezama%2C+and+Iddo+Drori%2E+2023%2E+Exploring+the+mit+mathematics+and+eecs+curriculum+using+large+)
25. Language Resource References Massimo Poesio, Yulia Grishina, Varada Kolhatkar, Nafise Moosavi, Ina Roesiger, Adam Roussel, Fabien Simonjetz, Alexandra Uma, Olga Uryupina, Juntao Yu, and Heike Zinsmeister. 2018. Anaphora resolution with the ARRAU corpus. In Proc. of the NAACL Worskhop on Computationa… [find](https://scholar.google.com/scholar?q=Language+Resource+References+Massimo+Poesio%2C+Yulia+Grishina%2C+Varada+Kolhatkar%2C+Nafise+Moosavi%2C+Ina+Roesiger%2C+Adam+Roussel%2C+Fabien+Simonjetz%2C+Alexandra+Uma%2C+Olga+Uryupina%2C+Juntao+Yu%2C+and+Heike+Zinsmeis) [our card](https://md.research.elamur.ai/free/papers/poesio-2018-anaphora-resolution-arrau-corpus/)
26. Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020. WINOGRANDE: An adversarial winograd schema challenge at scale. In Proc. of AAAI. [find](https://scholar.google.com/scholar?q=Keisuke+Sakaguchi%2C+Ronan+Le+Bras%2C+Chandra+Bhagavatula%2C+and+Yejin+Choi%2E+2020%2E+WINOGRANDE%3A+An+adversarial+winograd+schema+challenge+at+scale%2E+In+Proc%2E+of+AAAI%2E)
27. Juntao Yu, Sopan Khosla, Ramesh Manuvinakurike, Lori Levin, Vincent Ng, Massimo Poesio, Michael Strube, and Massimo Poesio. 2022. The CODI/CRAC 2022 shared task on anaphora resolution, bridging and discourse deixis in dialogue. In Proc. of CODI/CRAC Shared Task. [find](https://scholar.google.com/scholar?q=Juntao+Yu%2C+Sopan+Khosla%2C+Ramesh+Manuvinakurike%2C+Lori+Levin%2C+Vincent+Ng%2C+Massimo+Poesio%2C+Michael+Strube%2C+and+Massimo+Poesio%2E+2022%2E+The+CODI%2FCRAC+2022+shared+task+on+anaphora+resolution%2C+bridging+and+di)
28. Terry Winograd. 1972. Understanding natural language. Cognitive Psychology, 3(1):1–191. [find](https://scholar.google.com/scholar?q=Terry+Winograd%2E+1972%2E+Understanding+natural+language%2E+Cognitive+Psychology%2C+3%281%29%3A1%E2%80%93191%2E)
29. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of thoughts: Deliberate problem solving with large language models. [find](https://scholar.google.com/scholar?q=Shunyu+Yao%2C+Dian+Yu%2C+Jeffrey+Zhao%2C+Izhak+Shafran%2C+Thomas+L%2E+Griffiths%2C+Yuan+Cao%2C+and+Karthik+Narasimhan%2E+2023%2E+Tree+of+thoughts%3A+Deliberate+problem+solving+with+large+language+models%2E)
30. Juntao Yu, Nafise Moosavi, Silviu Paun, Sopan Khosla, Sameer Pradhan, and Massimo Poesio. 2022. The universal anaphora scorer 1.0. In Proc. of LREC. [find](https://scholar.google.com/scholar?q=Juntao+Yu%2C+Nafise+Moosavi%2C+Silviu+Paun%2C+Sopan+Khosla%2C+Sameer+Pradhan%2C+and+Massimo+Poesio%2E+2022%2E+The+universal+anaphora+scorer+1%2E0%2E+In+Proc%2E+of+LREC%2E) [our card](https://md.research.elamur.ai/free/papers/yu-2022-universal-anaphora-scorer/)
31. Juntao Yu, Nafise Moosavi, Silviu Paun, and Massimo Poesio. 2021. Stay together: A system for single and split-antecedent anaphora resolution. In Proc. of NAACL. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Juntao+Yu%2C+Nafise+Moosavi%2C+Silviu+Paun%2C+and+Massimo+Poesio%2E+2021%2E+Stay+together%3A+A+system+for+single+and+split%2Dantecedent+anaphora+resolution%2E+In+Proc%2E+of+NAACL%2E+Association+for+Computational+Linguist) [our card](https://md.research.elamur.ai/free/papers/yu-2021-stay-together-system-single-split/)
32. Juntao Yu, Alexandra Uma, and Massimo Poesio. 2020. A cluster ranking model for full anaphora resolution. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 11–20, Marseille, France. European Language Resources Association. [find](https://scholar.google.com/scholar?q=Juntao+Yu%2C+Alexandra+Uma%2C+and+Massimo+Poesio%2E+2020%2E+A+cluster+ranking+model+for+full+anaphora+resolution%2E+In+Proceedings+of+the+Twelfth+Language+Resources+and+Evaluation+Conference%2C+pages+11%E2%80%9320%2C+Marse) [our card](https://md.research.elamur.ai/free/papers/yu-2020-cluster-ranking-model-full-anaphora/)
33. Sarah J. Zhang, Samuel Florin, Ariel N. Lee, Ea-mon Niknafs, Andrei Marginean, Annie Wang, [find](https://scholar.google.com/scholar?q=Sarah+J%2E+Zhang%2C+Samuel+Florin%2C+Ariel+N%2E+Lee%2C+Ea%2Dmon+Niknafs%2C+Andrei+Marginean%2C+Annie+Wang%2C)