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.

该论文提出了一种跨语言句子选择的方法，旨在使用数据增强和负采样技术来直接学习基于嵌入的跨语言查询相关模型，结果显示该方法的效果不亚于或优于多个最先进的机器翻译 + 单语检索系统，该方法在英语 - 索马里语、英语 - 斯瓦希里语和英语 - 塔加洛语三种语言对中获得了一致的改进。