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Query language identification with weak supervision and noisy label pruning

By Sweta Sharma, Vijay Huddar, Ishita Aggarwal, Namrata Khoriya, Vishnu Narayanan, Atul Saroop, Rahul Bhagat
2021
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Query Language identification is an important part of a multilingual product search system. However, accurate language identification in product searches is difficult due to multiple reasons, including presence of noise in available datasets. In this work, we propose a learning framework that combines weak supervision with noisy label pruning. We use Convolutional Neural Networks (CNN) based models to carry out such a combination. Our results show improvements over FastText baselines and FastText with weak supervision, thereby demonstrating the benefit of such a combination.
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