Multilingual Coreference Resolution via Cycle-Consistent Machine Translation
Quick Answer
This study introduces a novel coreference resolution pipeline utilizing machine translation to enhance training data for low-resource languages.
Quick Take
By employing back-translation and cosine similarity with BERT, the method significantly improves coreference resolution performance, demonstrating effectiveness in languages lacking prior corpora.
Key Points
- Proposed pipeline uses machine translation to generate training data for low-resource languages.
- Back-translation assesses quality via cosine similarity in BERT's latent space.
- Significant performance gains observed in coreference resolution across four low-resource languages.
- Pipeline enables accurate resolution in languages without existing corpora.
Paper Resources
Source Excerpt
arXiv:2606. 05444v1 Announce Type: new Abstract: Coreference resolution is a core NLP task, having a broad range of downstream applications, e. g. ~machine translation, question answering, document summarization, etc. While the task is well-studied in English, comparatively less attention is dedicated to coreference resolution in other languages, especially low-resource ones.
To mitigate this gap, we propose a novel coreference resolution pipeline that harnesses machine translation (MT) from English to a target low-resource language, to generate or expand training data. …
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