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Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain

2022-12-05 10:33:59
Miguel Rios, Raluca-Maria Chereji, Alina Secara, Dragos Ciobanu
arXiv_CL
       
Abstract

Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.

Abstract (translated)

URL

https://arxiv.org/abs/2212.02143

PDF

https://arxiv.org/pdf/2212.02143.pdf

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