利用对抗性训练进行跨语言文本分类

Xin Dong, Yaxin Zhu, Yupeng Zhang, Zuohui Fu, Dongkuan Xu, Sen Yang, Gerard de Melo
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引用次数: 24

摘要

在跨语言文本分类中,人们试图利用一种语言的标记数据来训练一个文本分类模型,然后该模型可以应用于完全不同的语言。最近的多语言表示模型使实现这一目标变得更加容易。然而,在这样做时,语言之间可能仍然存在被忽视的细微差异。为了解决这个问题,我们提出了一个半监督对抗性训练过程,该过程最小化了保留标签的输入扰动的最大损失。然后,生成的模型作为老师,为未标记的目标语言样本诱导标签,这些标签可以在进一步的对抗训练中使用,从而使我们的模型逐渐适应目标语言。与许多强基线相比,我们观察到不同语言在文档和意图分类方面的有效性显著提高。
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Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification
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 lan- guage 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 signifi- cant gains in effectiveness on document and intent classification for a diverse set of languages.
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