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arXiv · 2106.02293

Cross-language Sentence Selection via Data Augmentation and Rationale Training

Yanda Chen·Chris Kedzie·Suraj Nair·Petra Galuščáková·Rui Zhang·Douglas W. Oard·Kathleen McKeown
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Abstract

This paper proposes an approach to cross-language sentence selection in a low-resource setting. It uses data augmentation and negative sampling techniques on noisy parallel sentence data to directly learn a cross-lingual embedding-based query relevance model. Results show that this approach performs as well as or better than multiple state-of-the-art machine translation + monolingual retrieval systems trained on the same parallel data. Moreover, when a rationale training secondary objective is applied to encourage the model to match word alignment hints from a phrase-based statistical machine translation model, consistent improvements are seen across three language pairs (English-Somali, English-Swahili and English-Tagalog) over a variety of state-of-the-art baselines.

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Yanda Chen, Chris Kedzie, Suraj Nair, Petra Galuščáková, Rui Zhang, Douglas W. Oard, Kathleen McKeown. 2021-06-04. Cross-language Sentence Selection via Data Augmentation and Rationale Training. https://arxiv.org/abs/2106.02293

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