Towards Generating Automatic Anaphora Annotations

Dima Taji; Daniel Zeman

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arXiv · 2503.09417

Towards Generating Automatic Anaphora Annotations

Dima Taji·Daniel Zeman

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Abstract

Training models that can perform well on various NLP tasks require large amounts of data, and this becomes more apparent with nuanced tasks such as anaphora and conference resolution. To combat the prohibitive costs of creating manual gold annotated data, this paper explores two methods to automatically create datasets with coreferential annotations; direct conversion from existing datasets, and parsing using multilingual models capable of handling new and unseen languages. The paper details the current progress on those two fronts, as well as the challenges the efforts currently face, and our approach to overcoming these challenges.

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Dima Taji, Daniel Zeman. 2025-03-12. Towards Generating Automatic Anaphora Annotations. https://arxiv.org/abs/2503.09417

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