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

Jihyeon Lee, Taehee Kim, Yunwon Tae, Cheonbok Park, Jaegul Choo

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).
Anthology ID:
2023.findings-eacl.18
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
239–253
Language:
URL:
https://aclanthology.org/2023.findings-eacl.18
DOI:
10.18653/v1/2023.findings-eacl.18
Bibkey:
Cite (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.
Cite (Informal):
PePe: Personalized Post-editing Model utilizing User-generated Post-edits (Lee et al., Findings 2023)
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