XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering

By Jasdeep Singh, Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, Richard Socher. 2019

85 citations recorded by the source corpus.

While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language. XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark. With improvements of up to $4.8\%$, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language. On the SQuAD question answering task, we see that XLDA provides a $1.0\%$ performance increase on the English evaluation set. Comprehensive experiments suggest that most languages are effective as cross-lingual augmentors, that XLDA is robust to a wide range of translation quality, and that XLDA is even more effective for randomly initialized models than for pretrained models.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Question Answering & Reading Comprehension

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Luke S. Zettlemoyer

Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, neutral): “, 2018) 3 × const Input 3 XLDA (Singh et al., 2019) × 3 Depends Input 3 SEQMIX (Zhang et al.”
Unsupervised Cross-lingual Representation Learning at Scale (2019, methodology): “Huang et al. (2019) showed gains over XLM using cross-lingual multi-task learning, and Singh et al. (2019) demonstrated the efﬁciency of cross-lingual data augmentation for cross-lingual NLI.”