Journal article Open Access

# Revisiting Multi-Domain Machine Translation

MinhQuang Pham; Josep Crego; François Yvon

### Citation Style Language JSON Export

{
"publisher": "Zenodo",
"DOI": "10.5281/zenodo.4537184",
"container_title": "Transactions of the Association for Computational Linguistics",
"language": "eng",
"title": "Revisiting Multi-Domain Machine Translation",
"issued": {
"date-parts": [
[
2021,
2,
12
]
]
},
"abstract": "<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>",
"author": [
{
"family": "MinhQuang Pham"
},
{
"family": "Josep Crego"
},
{
"family": "Fran\u00e7ois Yvon"
}
],
"volume": "9",
"type": "article-journal",
"id": "4537184"
}
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