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

Abstract

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.

ACL 2019, short paper

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• A Deep Relevance Matching Model for Ad-hoc Retrieval
• End-to-End Neural Ad-hoc Ranking with Kernel Pooling
• Enriching Word Vectors with Subword Information
• A Dual Embedding Space Model for Document Ranking
• A Study of MatchPyramid Models on Ad-hoc Retrieval
• Deep Relevance Ranking Using Enhanced Document-Query Interactions

Cited by in corpus (2)

• Cross-language Sentence Selection via Data Augmentation and Rationale Training
• Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce