Multilingual Coreference Resolution in Low-resource South Asian Languages

• URL: http://arxiv.org/abs/2402.13571v2
• Date: Sat, 23 Mar 2024 08:22:58 GMT
• Title: Multilingual Coreference Resolution in Low-resource South Asian Languages
• Authors: Ritwik Mishra, Pooja Desur, Rajiv Ratn Shah, Ponnurangam Kumaraguru,
• Abstract summary: We introduce a Translated dataset for Multilingual Coreference Resolution (TransMuCoRes) in 31 South Asian languages. Nearly all of the predicted translations successfully pass a sanity check, and 75% of English references align with their predicted translations. This study is the first to evaluate an end-to-end coreference resolution model on a Hindi golden set.
• Score: 36.31301773167754
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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