February 12, 2021 Journal article Open Access

MinhQuang Pham; Josep Crego; François Yvon

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    "description": "<p>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 un-expected 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.</p>", 
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    "title": "Revisiting Multi-Domain Machine Translation", 
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      "title": "Transactions of the Association for Computational Linguistics"
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    "publication_date": "2021-02-12", 
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