In this paper, we propose to boost low-resource cross-lingual document
retrieval performance with deep bilingual query-document representations. We
match queries and documents in both source and target languages with four
components, each of which is implemented as a term interaction-based deep
neural network with cross-lingual word embeddings as input. By including query
likelihood scores as extra features, our model effectively learns to rerank the
retrieved documents by using a small number of relevance labels for
low-resource language pairs. Due to the shared cross-lingual word embedding
space, the model can also be directly applied to another language pair without
any training label. Experimental results on the MATERIAL dataset show that our
model outperforms the competitive translation-based baselines on
English-Swahili, English-Tagalog, and English-Somali cross-lingual information
retrieval tasks.

本文提出了一种使用深度双语查询 - 文档表示来提高低资源跨语言文档检索性能的方法，并通过包括查询似然得分等额外特征，有效学习使用少量相关性标签为低资源语言对重新排序检索到的文档的模型。实验结果表明，本模型在 MATERIAL 数据集上优于竞争的基于翻译的对英斯瓦希里语、英语 - 塔加洛语和英语 - 索马里语跨语言信息检索任务的基线模型。