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In this paper, we propose to boost low-resource cross-lingual documentretrieval performance with deep bilingual query-document representations. Wematch queries and documents in both source and target languages with fourcomponents, each of which is implemented as a term interaction-based deepneural network with cross-lingual word embeddings as input. By including querylikelihood scores as extra features, our model effectively learns to rerank theretrieved documents by using a small number of relevance labels forlow-resource language pairs. Due to the shared cross-lingual word embeddingspace, the model can also be directly applied to another language pair withoutany training label. Experimental results on the MATERIAL dataset show that ourmodel outperforms the competitive translation-based baselines onEnglish-Swahili, English-Tagalog, and English-Somali cross-lingual informationretrieval tasks.

Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations