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        <title>PePe: Personalized Post-editing Model utilizing User-generated Post-edits</title>
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    <name type="personal">
        <namePart type="given">Jihyeon</namePart>
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        <namePart type="given">Taehee</namePart>
        <namePart type="family">Kim</namePart>
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    <name type="personal">
        <namePart type="given">Yunwon</namePart>
        <namePart type="family">Tae</namePart>
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    <name type="personal">
        <namePart type="given">Cheonbok</namePart>
        <namePart type="family">Park</namePart>
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    <name type="personal">
        <namePart type="given">Jaegul</namePart>
        <namePart type="family">Choo</namePart>
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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).</abstract>
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%0 Conference Proceedings
%T PePe: Personalized Post-editing Model utilizing User-generated Post-edits
%A Lee, Jihyeon
%A Kim, Taehee
%A Tae, Yunwon
%A Park, Cheonbok
%A Choo, Jaegul
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F lee-etal-2023-pepe
%X 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).
%R 10.18653/v1/2023.findings-eacl.18
%U https://aclanthology.org/2023.findings-eacl.18
%U https://doi.org/10.18653/v1/2023.findings-eacl.18
%P 239-253

Markdown (Informal)

[PePe: Personalized Post-editing Model utilizing User-generated Post-edits](https://aclanthology.org/2023.findings-eacl.18) (Lee et al., Findings 2023)

• PePe: Personalized Post-editing Model utilizing User-generated Post-edits (Lee et al., Findings 2023)

ACL

• Jihyeon Lee, Taehee Kim, Yunwon Tae, Cheonbok Park, and Jaegul Choo. 2023. PePe: Personalized Post-editing Model utilizing User-generated Post-edits. In Findings of the Association for Computational Linguistics: EACL 2023, pages 239–253, Dubrovnik, Croatia. Association for Computational Linguistics.