mDAPT: Multilingual Domain Adaptive Pretraining in a Single Model

Rasmus Kær Jørgensen, Mareike Hartmann, Xiang Dai, Desmond Elliott


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.
Anthology ID:
2021.findings-emnlp.290
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3404–3418
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.290
DOI:
10.18653/v1/2021.findings-emnlp.290
Bibkey:
Cite (ACL):
Rasmus Kær Jørgensen, Mareike Hartmann, Xiang Dai, and Desmond Elliott. 2021. mDAPT: Multilingual Domain Adaptive Pretraining in a Single Model. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3404–3418, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
mDAPT: Multilingual Domain Adaptive Pretraining in a Single Model (Kær Jørgensen et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.290.pdf
Code
 rasmuskaer/mdapt_supplements
Data
NCBI Disease