Cross-lingual Information Retrieval with BERT
This addresses the problem of retrieving documents in different languages for users, but it is incremental as it applies an existing method to a specific domain.
The paper tackled cross-lingual information retrieval by using a BERT-based model to match English queries with foreign-language documents, achieving effectiveness and outperforming baseline approaches in retrieving Lithuanian documents.
Multiple neural language models have been developed recently, e.g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking. In this paper, we explore the use of the popular bidirectional language model, BERT, to model and learn the relevance between English queries and foreign-language documents in the task of cross-lingual information retrieval. A deep relevance matching model based on BERT is introduced and trained by finetuning a pretrained multilingual BERT model with weak supervision, using home-made CLIR training data derived from parallel corpora. Experimental results of the retrieval of Lithuanian documents against short English queries show that our model is effective and outperforms the competitive baseline approaches.