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arXiv · 2209.10139

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

Jihyeon Lee·Taehee Kim·Yunwon Tae·Cheonbok Park·Jaegul Choo
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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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Jihyeon Lee, Taehee Kim, Yunwon Tae, Cheonbok Park, Jaegul Choo. 2022-09-21. PePe: Personalized Post-editing Model utilizing User-generated Post-edits. https://arxiv.org/abs/2209.10139

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