Learning Cross-Lingual IR from an English Retriever

• URL: http://arxiv.org/abs/2112.08185v1
• Date: Wed, 15 Dec 2021 15:07:54 GMT
• Title: Learning Cross-Lingual IR from an English Retriever
• Authors: Yulong Li, Martin Franz, Md Arafat Sultan, Bhavani Iyer, Young-Suk Lee, Avirup Sil
• Abstract summary: The proposed model is far more effective than the existing approach of fine-tuning with cross-lingual labeled IR data, with a gain in accuracy of 25.4 Recall@5kt.
• Score: 10.27108918912692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a new cross-lingual information retrieval (CLIR) model trained using multi-stage knowledge distillation (KD). The teacher and the student are heterogeneous systems-the former is a pipeline that relies on machine translation and monolingual IR, while the latter executes a single CLIR operation. We show that the student can learn both multilingual representations and CLIR by optimizing two corresponding KD objectives. Learning multilingual representations from an English-only retriever is accomplished using a novel cross-lingual alignment algorithm that greedily re-positions the teacher tokens for alignment. Evaluation on the XOR-TyDi benchmark shows that the proposed model is far more effective than the existing approach of fine-tuning with cross-lingual labeled IR data, with a gain in accuracy of 25.4 Recall@5kt.

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