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March 8, 2026Open Access

The Generalization Ability of Coreference Resolution Systems

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Authors

HCHaixia Chai

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Overview

Analysis of coreference resolution systems shows varied effectiveness across languages and genres, highlighting the need for evaluation improvements.

Key Points

  • This research aims to assess the generalization capabilities of coreference resolution systems across various contexts and languages.
  • Investigated multilingual coreference resolution using universal annotations and error analysis.
  • Integrated linguistic features from universal morphosyntactic annotations into a baseline model.
  • Developed a neural coreference model using discourse structure from centering theory and weighted graphs.
  • Conducted extrinsic and intrinsic evaluations on a community-based question answering task and created a noisy text dataset.
  • Identified common patterns in coreference resolution across languages, particularly with overt pronouns.
  • Achieved an 80.9 F1 score with the neural model, outperforming previous baselines.
  • Found that rule-based systems demonstrate consistent performance in community question answering compared to state-of-the-art models.
  • Highlighted discrepancies between intrinsic metrics and extrinsic evaluations, suggesting a need for combined evaluations.

Cite This Study

Haixia Chai (2026) studied this question.

synapsesocial.com/papers/69ada804bc08abd80d5bb343https://doi.org/10.11588/heidok.00037927
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Controlled Reevaluation of Coreference Resolution Models2024
  2. 2BOOKCOREF: Coreference Resolution at Book Scale2025
  3. 3Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?2025
  4. 4Performance Analysis of End-to-End Neural Coreference Resolution in English and Indonesian Texts2024 · 1 citations
  5. 5Multilingual Coreference Resolution in Low-resource South Asian Languages2024