# Towards User-Driven Neural Machine Translation

arXiv: https://arxiv.org/abs/2106.06200 · HTML: https://mlsanity.com/paper/2106.06200/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Sequence-to-Sequence Learning as Beam-Search Optimization](https://mlsanity.com/paper/1606.02960/) (2016)
- [Neural Machine Translation with Extended Context](https://mlsanity.com/paper/1708.05943/) (2017)
- [Curriculum Learning for Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/1905.05816/) (2019)
- [Personalized Machine Translation: Preserving Original Author Traits](https://mlsanity.com/paper/1610.05461/) (2016)
- [Learning to Remember Translation History with a Continuous Cache](https://mlsanity.com/paper/1711.09367/) (2017)
- [Extreme Adaptation for Personalized Neural Machine Translation](https://mlsanity.com/paper/1805.01817/) (2018)
- [Self-Paced Learning for Neural Machine Translation](https://mlsanity.com/paper/2010.04505/) (2020)
- [Adversarial Contrastive Estimation](https://mlsanity.com/paper/1805.03642/) (2018)
- [Contrastive Unsupervised Word Alignment with Non-Local Features](https://mlsanity.com/paper/1410.2082/) (2014)
- [Iterative Dual Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1912.07239/) (2019)

## Built on by (most cited first, 10 of 10)

- [On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models](https://mlsanity.com/paper/2403.04204/) (2024)
- [Towards Making the Most of Dialogue Characteristics for Neural Chat Translation](https://mlsanity.com/paper/2109.00668/) (2021)
- [Frequency-Aware Contrastive Learning for Neural Machine Translation](https://mlsanity.com/paper/2112.14484/) (2021)
- [Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task](https://mlsanity.com/paper/2210.09683/) (2022)
- [Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task](https://mlsanity.com/paper/2210.10049/) (2022)
- [Steering Large Language Models for Machine Translation Personalization](https://mlsanity.com/paper/2505.16612/) (2025)
- [PePe: Personalized Post-editing Model utilizing User-generated Post-edits](https://mlsanity.com/paper/2209.10139/) (2022)
- [Diverse Sign Language Translation](https://mlsanity.com/paper/2410.19586/) (2024)
- [Unified Model Learning for Various Neural Machine Translation](https://mlsanity.com/paper/2305.02777/) (2023)
- [Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/2309.02640/) (2023)

## Abstract (arXiv)

A good translation should not only translate the original content semantically, but also incarnate personal traits of the original text. For a real-world neural machine translation (NMT) system, these user traits (e.g., topic preference, stylistic characteristics and expression habits) can be preserved in user behavior (e.g., historical inputs). However, current NMT systems marginally consider the user behavior due to: 1) the difficulty of modeling user portraits in zero-shot scenarios, and 2) the lack of user-behavior annotated parallel dataset. To fill this gap, we introduce a novel framework called user-driven NMT. Specifically, a cache-based module and a user-driven contrastive learning method are proposed to offer NMT the ability to capture potential user traits from their historical inputs under a zero-shot learning fashion. Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus. Experimental results confirm that the proposed user-driven NMT can generate user-specific translations.