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        <title>Investigating the Effectiveness of Multiple Expert Models Collaboration</title>
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    <abstract>This paper aims to investigate the effectiveness of several machine translation (MT) models and aggregation methods in a multi-domain setting under fair conditions and explore a direction for tackling multi-domain MT. We mainly compare the performance of the single model approach by jointly training all domains and the multi-expert models approach with a particular aggregation strategy. We conduct experiments on multiple domain datasets and demonstrate that a combination of smaller domain expert models can outperform a larger model trained for all domain data.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.960</identifier>
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%0 Conference Proceedings
%T Investigating the Effectiveness of Multiple Expert Models Collaboration
%A Ito, Ikumi
%A Ito, Takumi
%A Suzuki, Jun
%A Inui, Kentaro
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ito-etal-2023-investigating
%X This paper aims to investigate the effectiveness of several machine translation (MT) models and aggregation methods in a multi-domain setting under fair conditions and explore a direction for tackling multi-domain MT. We mainly compare the performance of the single model approach by jointly training all domains and the multi-expert models approach with a particular aggregation strategy. We conduct experiments on multiple domain datasets and demonstrate that a combination of smaller domain expert models can outperform a larger model trained for all domain data.
%R 10.18653/v1/2023.findings-emnlp.960
%U https://aclanthology.org/2023.findings-emnlp.960
%U https://doi.org/10.18653/v1/2023.findings-emnlp.960
%P 14393-14404

Markdown (Informal)

[Investigating the Effectiveness of Multiple Expert Models Collaboration](https://aclanthology.org/2023.findings-emnlp.960) (Ito et al., Findings 2023)

• Investigating the Effectiveness of Multiple Expert Models Collaboration (Ito et al., Findings 2023)

ACL

• Ikumi Ito, Takumi Ito, Jun Suzuki, and Kentaro Inui. 2023. Investigating the Effectiveness of Multiple Expert Models Collaboration. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14393–14404, Singapore. Association for Computational Linguistics.