@inproceedings{ito-etal-2023-investigating,
    title = "Investigating the Effectiveness of Multiple Expert Models Collaboration",
    author = "Ito, Ikumi  and
      Ito, Takumi  and
      Suzuki, Jun  and
      Inui, Kentaro",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2023.findings-emnlp.960/",
    doi = "10.18653/v1/2023.findings-emnlp.960",
    pages = "14393--14404",
    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."
}