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Qualifying Exam Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification
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Speaker: Xin Dong

Location : Remote via Webex

Committee: 

Gerard de Melo (Chair), Yongfeng Zhang, Karl Stratos, Srinivas Narayana

Event Type: Qualifying Exam

Abstract: In cross-lingual text classification, one seeks to exploit labeled data from one language to train a text classification model that can then be applied to a completely different language. Recent multilingual representation models have made it much easier to achieve this. Still, there may still be subtle differences between languages that are neglected when doing so. To address this, we present a semi- supervised adversarial training process that minimizes the maximal loss for label-preserving input perturbations. The resulting model then serves as a teacher to induce labels for unlabeled target language samples that can be used during further adversarial training, allowing us to gradually adapt our model to the target language. Compared with a number of strong baselines, we observe significant gains in effectiveness on document and intent classification for a diverse set of languages.

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Meeting number: 192 300 048
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