  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.
