Multilingual Coreference Resolution in Low-resource South Asian Languages
<|reference_start|>Multilingual Coreference Resolution in Low-resource South Asian Languages: Coreference resolution involves the task of identifying text spans within a discourse that pertain to the same real-world entity. While this task has been extensively explored in the English language, there has been a notable scarcity of publicly accessible resources and models for coreference resolution in South Asian languages. We introduce a Translated dataset for Multilingual Coreference Resolution (TransMuCoRes) in 31 South Asian languages using off-the-shelf tools for translation and word-alignment. Nearly all of the predicted translations successfully pass a sanity check, and 75% of English references align with their predicted translations. Using multilingual encoders, two off-the-shelf coreference resolution models were trained on a concatenation of TransMuCoRes and a Hindi coreference resolution dataset with manual annotations. The best performing model achieved a score of 64 and 68 for LEA F1 and CoNLL F1, respectively, on our test-split of Hindi golden set. This study is the first to evaluate an end-to-end coreference resolution model on a Hindi golden set. Furthermore, this work underscores the limitations of current coreference evaluation metrics when applied to datasets with split antecedents, advocating for the development of more suitable evaluation metrics.<|reference_end|>
@article{mishra2024multilingual,
    title={Multilingual Coreference Resolution in Low-resource South Asian
  Languages},
    author={Ritwik Mishra, Pooja Desur, Rajiv Ratn Shah, Ponnurangam Kumaraguru},
    journal={arXiv preprint arXiv:2402.13571},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13571},
    primaryClass={cs.CL cs.AI}
}