PePe: Personalized Post-editing Model utilizing User-generated Post-edits
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).
Code (0)
등록된 구현이 없습니다.
Tasks
Automatic Post-EditingMachine TranslationTranslationSimilar Papers 제목 키워드 기반
LIPE: Learning Personalized Identity Prior for Non-rigid Image Editing
Although recent years have witnessed significant advancements in image editing thanks to the remarkable progress of text-to-image diffusion models, the problem of non-rigid image editing still presents its complexities a…
Season combinatorial intervention predictions with Salt & Peper
Interventions play a pivotal role in the study of complex biological systems. In drug discovery, genetic interventions (such as CRISPR base editing) have become central to both identifying potential therapeutic targets a…
Drug DiscoveryNavigatePersonalized Response Generation via Generative Split Memory Network
Despite the impressive successes of generation and dialogue systems, how to endow a text generation system with particular personality traits to deliver more personalized responses remains under-investigated. In this wor…
Response GenerationText GenerationCCEdit: Creative and Controllable Video Editing via Diffusion Models
In this paper, we present CCEdit, a versatile generative video editing framework based on diffusion models. Our approach employs a novel trident network structure that separates structure and appearance control, ensuring…
Image GenerationText-to-Image GenerationVideo EditingPersonalized Image Editing in Text-to-Image Diffusion Models via Collaborative Direct Preference Optimization
Text-to-image (T2I) diffusion models have made remarkable strides in generating and editing high-fidelity images from text. Yet, these models remain fundamentally generic, failing to adapt to the nuanced aesthetic prefer…
Graph Neural NetworkImage Editing