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CorPipe 26 wins CRAC 2026 with novel empty node prediction

By PulseAugur Editorial · [3 sources] ·

Researchers have developed CorPipe 26, a system that won the CRAC 2026 Shared Task on Multilingual Coreference Resolution. This improved system introduces a variant capable of predicting empty nodes alongside mentions and coreference links within a single model. CorPipe 26 demonstrated superior performance, outperforming other submissions in both the LLM and unconstrained tracks by significant margins. AI

IMPACT Advances multilingual coreference resolution, potentially improving cross-lingual understanding in NLP applications.

RANK_REASON Academic paper detailing a new system and its performance on a specific task.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

CorPipe 26 wins CRAC 2026 with novel empty node prediction

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COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · Milan Straka ·

    CorPipe at CRAC 2026: Empty Nodes and Cross-Lingual Transfer in Multilingual Coreference Resolution

    arXiv:2605.30133v1 Announce Type: new Abstract: We introduce CorPipe 26, our winning submission to the CRAC 2026 Shared Task on Multilingual Coreference Resolution. The fifth edition of this shared task focuses mainly on the comparison of generative LLMs and specialized systems; …

  2. arXiv cs.CL TIER_1 English(EN) · Milan Straka ·

    CorPipe at CRAC 2026: Empty Nodes and Cross-Lingual Transfer in Multilingual Coreference Resolution

    We introduce CorPipe 26, our winning submission to the CRAC 2026 Shared Task on Multilingual Coreference Resolution. The fifth edition of this shared task focuses mainly on the comparison of generative LLMs and specialized systems; additionally, 5 more datasets and 2 new language…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    CorPipe at CRAC 2026: Empty Nodes and Cross-Lingual Transfer in Multilingual Coreference Resolution

    We introduce CorPipe 26, our winning submission to the CRAC 2026 Shared Task on Multilingual Coreference Resolution. The fifth edition of this shared task focuses mainly on the comparison of generative LLMs and specialized systems; additionally, 5 more datasets and 2 new language…

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