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        <title>m⌃4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter</title>
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    <abstract>Multilingual neural machine translation models (MNMT) yield state-of-the-art performance when evaluated on data from a domain and language pair seen at training time. However, when a MNMT model is used to translate under domain shift or to a new language pair, performance drops dramatically. We consider a very challenging scenario: adapting the MNMT model both to a new domain and to a new language pair at the same time. In this paper, we propose m⌃4Adapter (Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter), which combines domain and language knowledge using meta-learning with adapters. We present results showing that our approach is a parameter-efficient solution which effectively adapts a model to both a new language pair and a new domain, while outperforming other adapter methods. An ablation study also shows that our approach more effectively transfers domain knowledge across different languages and language information across different domains.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.315</identifier>
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%0 Conference Proceedings
%T m⌃4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter
%A Lai, Wen
%A Chronopoulou, Alexandra
%A Fraser, Alexander
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F lai-etal-2022-4
%X Multilingual neural machine translation models (MNMT) yield state-of-the-art performance when evaluated on data from a domain and language pair seen at training time. However, when a MNMT model is used to translate under domain shift or to a new language pair, performance drops dramatically. We consider a very challenging scenario: adapting the MNMT model both to a new domain and to a new language pair at the same time. In this paper, we propose m⌃4Adapter (Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter), which combines domain and language knowledge using meta-learning with adapters. We present results showing that our approach is a parameter-efficient solution which effectively adapts a model to both a new language pair and a new domain, while outperforming other adapter methods. An ablation study also shows that our approach more effectively transfers domain knowledge across different languages and language information across different domains.
%R 10.18653/v1/2022.findings-emnlp.315
%U https://aclanthology.org/2022.findings-emnlp.315
%U https://doi.org/10.18653/v1/2022.findings-emnlp.315
%P 4282-4296

Markdown (Informal)

[m^4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter](https://aclanthology.org/2022.findings-emnlp.315) (Lai et al., Findings 2022)

• m^4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter (Lai et al., Findings 2022)

ACL

• Wen Lai, Alexandra Chronopoulou, and Alexander Fraser. 2022. m^4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4282–4296, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.