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Low-resource text classification using domain-adversarial learning

机译:使用领域对抗学习的低资源文本分类

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Deep learning techniques have recently shown to be successful in many natural language processing tasks forming state-of-the-art systems. They require, however, a large amount of annotated data which is often missing. This paper explores the use of domain-adversarial learning as a regularizer to avoid overfitting when training domain invariant features for deep, complex neural networks in low-resource and zero-resource settings in new target domains or languages. In case of new languages, we show that monolingual word vectors can be directly used for training without prealignment. Their projection into a common space can be learnt ad-hoc at training time reaching the final performance of pretrained multilingual word vectors.
机译:深度学习技术最近在形成自然语言系统的许多自然语言处理任务中取得了成功。但是,它们需要大量的注释数据,而这些数据通常会丢失。本文探讨了在训练新目标领域或语言中的低资源和零资源设置的深度,复杂神经网络的领域不变特征时,如何使用领域对抗学习作为正则化函数来避免过度拟合。在使用新语言的情况下,我们表明单语单词向量可以直接用于训练而无需预先对齐。它们在公共空间的投影可以在训练时临时学习,以达到预训练的多语言单词向量的最终性能。

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