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. To automatically validate
The proliferation of advanced AI models and the increasing demand for multilingual applications are driving a renewed focus on coreference resolution in diverse linguistic contexts.
Improving coreference resolution in low-resource languages is crucial for expanding AI's utility globally, enabling more effective cross-lingual information processing and application development.
This research outlines a method to leverage existing English resources and machine translation to create or augment training data for coreference resolution in languages with limited datasets, democratizing access to this NLP capability.
- · NLP researchers
- · Developers of multilingual AI applications
- · Users in non-English speaking regions
- · Machine translation providers
- · Monolingual NLP approaches
Enhanced performance of natural language understanding systems in a wider array of languages becomes possible.
This could accelerate the development of AI agents and other advanced applications reliant on deep language understanding in underserved linguistic markets.
Increased linguistic diversity in AI could foster more inclusive and equitable technology development, potentially shifting power dynamics in global tech leadership over time.
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