Towards Best Practices for Training Multilingual Dense Retrieval Models

• URL: http://arxiv.org/abs/2204.02363v1
• Date: Tue, 5 Apr 2022 17:12:53 GMT
• Title: Towards Best Practices for Training Multilingual Dense Retrieval Models
• Authors: Xinyu Zhang, Kelechi Ogueji, Xueguang Ma, Jimmy Lin
• Abstract summary: We focus on the task of monolingual retrieval in a variety of typologically diverse languages using one such design. Our study is organized as a "best practices" guide for training multilingual dense retrieval models.
• Score: 54.91016739123398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dense retrieval models using a transformer-based bi-encoder design have emerged as an active area of research. In this work, we focus on the task of monolingual retrieval in a variety of typologically diverse languages using one such design. Although recent work with multilingual transformers demonstrates that they exhibit strong cross-lingual generalization capabilities, there remain many open research questions, which we tackle here. Our study is organized as a "best practices" guide for training multilingual dense retrieval models, broken down into three main scenarios: where a multilingual transformer is available, but relevance judgments are not available in the language of interest; where both models and training data are available; and, where training data are available not but models. In considering these scenarios, we gain a better understanding of the role of multi-stage fine-tuning, the strength of cross-lingual transfer under various conditions, the usefulness of out-of-language data, and the advantages of multilingual vs. monolingual transformers. Our recommendations offer a guide for practitioners building search applications, particularly for low-resource languages, and while our work leaves open a number of research questions, we provide a solid foundation for future work.

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