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

Cross-language Sentence Selection via Data Augmentation and Rationale Training