Cross-language Sentence Selection via Data Augmentation and Rationale Training

• URL: http://arxiv.org/abs/2106.02293v1
• Date: Fri, 4 Jun 2021 07:08:47 GMT
• Title: Cross-language Sentence Selection via Data Augmentation and Rationale Training
• Authors: Yanda Chen, Chris Kedzie, Suraj Nair, Petra Galu\v{s}\v{c}\'akov\'a, Rui Zhang, Douglas W. Oard, Kathleen McKeown
• Abstract summary: It uses data augmentation and negative sampling techniques on noisy parallel sentence data to 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.
• Score: 22.106577427237635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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