Revisiting Multi-Domain Machine Translation

• Published in: Transactions of the Association for Computational Linguistics Vol. 9; pp. 17 - 35
• Main Authors: Pham, MinhQuang, Crego, Josep Maria, Yvon, François
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.01.2021; MIT Press Journals, The; The MIT Press
• Subjects: Adaptation; Computation and Language; Computational linguistics; Computer Science; Linguistics; Machine translation; Natural language processing; domain adaptation; Machine Translation
• ISSN: 2307-387X; 2307-387X
• DOI: 10.1162/tacl_a_00351

When building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from unexpected domains in testing. This multi-domain scenario has attracted a lot of recent work that fall under the general umbrella of transfer learning. In this study, we revisit multi-domain machine translation, with the aim to formulate the motivations for developing such systems and the associated expectations with respect to performance. Our experiments with a large sample of multi-domain systems show that most of these expectations are hardly met and suggest that further work is needed to better analyze the current behaviour of multi-domain systems and to make them fully hold their promises.