Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification

TL;DR

This paper introduces a semi-supervised adversarial training method that enhances cross-lingual text classification by better adapting models to target languages, showing significant improvements over existing baselines.

Contribution

It proposes a novel adversarial training approach that leverages unlabeled data for improved cross-lingual classification performance.

Findings

Significant accuracy gains on document and intent classification tasks.

Effective adaptation across diverse languages.

Outperforms strong baseline methods.

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.