MDAPT: Multilingual Domain Adaptive Pretraining in a Single Model

• URL: http://arxiv.org/abs/2109.06605v1
• Date: Tue, 14 Sep 2021 11:50:26 GMT
• Title: MDAPT: Multilingual Domain Adaptive Pretraining in a Single Model
• Authors: Rasmus K{\ae}r J{\o}rgensen and Mareike Hartmann and Xiang Dai and Desmond Elliott
• Abstract summary: We show that a single multilingual domain-specific model can outperform the general multilingual model. We propose different techniques to compose pretraining corpora that enable a language model to both become domain-specific and multilingual.
• Score: 17.566140528671134
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain adaptive pretraining, i.e. the continued unsupervised pretraining of a language model on domain-specific text, improves the modelling of text for downstream tasks within the domain. Numerous real-world applications are based on domain-specific text, e.g. working with financial or biomedical documents, and these applications often need to support multiple languages. However, large-scale domain-specific multilingual pretraining data for such scenarios can be difficult to obtain, due to regulations, legislation, or simply a lack of language- and domain-specific text. One solution is to train a single multilingual model, taking advantage of the data available in as many languages as possible. In this work, we explore the benefits of domain adaptive pretraining with a focus on adapting to multiple languages within a specific domain. We propose different techniques to compose pretraining corpora that enable a language model to both become domain-specific and multilingual. Evaluation on nine domain-specific datasets-for biomedical named entity recognition and financial sentence classification-covering seven different languages show that a single multilingual domain-specific model can outperform the general multilingual model, and performs close to its monolingual counterpart. This finding holds across two different pretraining methods, adapter-based pretraining and full model pretraining.

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