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

arXiv: https://arxiv.org/abs/2209.10139 · HTML: https://mlsanity.com/paper/2209.10139/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [Personalized Machine Translation: Preserving Original Author Traits](https://mlsanity.com/paper/1610.05461/) (2016)
- [Extreme Adaptation for Personalized Neural Machine Translation](https://mlsanity.com/paper/1805.01817/) (2018)
- [Compact Personalized Models for Neural Machine Translation](https://mlsanity.com/paper/1811.01990/) (2018)
- [MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing](https://mlsanity.com/paper/1809.00188/) (2018)
- [A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning](https://mlsanity.com/paper/1906.06253/) (2019)
- [Towards User-Driven Neural Machine Translation](https://mlsanity.com/paper/2106.06200/) (2021)
- [The AMU-UEDIN Submission to the WMT16 News Translation Task: Attention-based NMT Models as Feature Functions in Phrase-based SMT](https://mlsanity.com/paper/1605.04809/) (2016)
- [An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing](https://mlsanity.com/paper/1706.04138/) (2017)
- [A User-Study on Online Adaptation of Neural Machine Translation to Human Post-Edits](https://mlsanity.com/paper/1712.04853/) (2017)
- [Netmarble AI Center's WMT21 Automatic Post-Editing Shared Task Submission](https://mlsanity.com/paper/2109.06515/) (2021)
- [Learning to Copy for Automatic Post-Editing](https://mlsanity.com/paper/1911.03627/) (2019)

## Built on by (most cited first, 1 of 1)

- [Steering Large Language Models for Machine Translation Personalization](https://mlsanity.com/paper/2505.16612/) (2025)

## Abstract (arXiv)

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).