Cross-lingual Information Retrieval with BERT

• URL: http://arxiv.org/abs/2004.13005v1
• Date: Fri, 24 Apr 2020 23:32:13 GMT
• Title: Cross-lingual Information Retrieval with BERT
• Authors: Zhuolin Jiang, Amro El-Jaroudi, William Hartmann, Damianos Karakos, Lingjun Zhao
• Abstract summary: We explore the use of the popular bidirectional language model, BERT, to model and learn the relevance between English queries and foreign-language documents. A deep relevance matching model based on BERT is introduced and trained by finetuning a pretrained multilingual BERT model with weak supervision. Experimental results of the retrieval of Lithuanian documents against short English queries show that our model is effective and outperforms the competitive baseline approaches.
• Score: 8.052497255948046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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