PePe: Personalized Post-editing Model utilizing User-generated Post-edits

• URL: http://arxiv.org/abs/2209.10139v2
• Date: Thu, 13 Apr 2023 08:38:25 GMT
• Title: PePe: Personalized Post-editing Model utilizing User-generated Post-edits
• Authors: Jihyeon Lee, Taehee Kim, Yunwon Tae, Cheonbok Park, Jaegul Choo
• Abstract summary: We introduce a personalized automatic post-editing framework to address this challenge. We first collect post-editing data that connotes the user preference from a live machine translation system. We then propose a model that combines a discriminator module and user-specific parameters on the APE framework.
• Score: 28.749742163017544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Incorporating personal preference is crucial in advanced machine translation tasks. Despite the recent advancement of machine translation, it remains a demanding task to properly reflect personal style. In this paper, we introduce a personalized automatic post-editing framework to address this challenge, which effectively generates sentences considering distinct personal behaviors. To build this framework, we first collect post-editing data that connotes the user preference from a live machine translation system. Specifically, real-world users enter source sentences for translation and edit the machine-translated outputs according to the user's preferred style. We then propose a model that combines a discriminator module and user-specific parameters on the APE framework. Experimental results show that the proposed method outperforms other baseline models on four different metrics (i.e., BLEU, TER, YiSi-1, and human evaluation).

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