# Extreme Adaptation for Personalized Neural Machine Translation

arXiv: https://arxiv.org/abs/1805.01817 · HTML: https://mlsanity.com/paper/1805.01817/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2018.

## Built on by (most cited first, 27 of 27)

- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://mlsanity.com/paper/2106.10199/) (2021)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Tool Learning with Foundation Models](https://mlsanity.com/paper/2304.08354/) (2023)
- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality](https://mlsanity.com/paper/2205.04022/) (2022)
- [On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey](https://mlsanity.com/paper/2211.03154/) (2022)
- [Machine Translation Verbosity Control for Automatic Dubbing](https://mlsanity.com/paper/2110.03847/) (2021)
- [FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation](https://mlsanity.com/paper/2210.00193/) (2022)
- [Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation](https://mlsanity.com/paper/2103.13678/) (2021)
- [What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study](https://mlsanity.com/paper/2410.00545/) (2024)
- [Towards User-Driven Neural Machine Translation](https://mlsanity.com/paper/2106.06200/) (2021)
- [RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation](https://mlsanity.com/paper/2305.17131/) (2023)
- [Towards Universality in Multilingual Text Rewriting](https://mlsanity.com/paper/2107.14749/) (2021)
- [Controlling Styles in Neural Machine Translation with Activation Prompt](https://mlsanity.com/paper/2212.08909/) (2022)
- [Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation](https://mlsanity.com/paper/2310.18119/) (2023)
- [Controlling Extra-Textual Attributes about Dialogue Participants -- A Case Study of English-to-Polish Neural Machine Translation](https://mlsanity.com/paper/2205.04747/) (2022)
- [Reference-less Analysis of Context Specificity in Translation with Personalised Language Models](https://mlsanity.com/paper/2303.16618/) (2023)
- [Steering Large Language Models for Machine Translation Personalization](https://mlsanity.com/paper/2505.16612/) (2025)
- [MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation](https://mlsanity.com/paper/2305.15904/) (2023)
- [How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models?](https://mlsanity.com/paper/2309.08565/) (2023)
- [Synthesizing Monolingual Data for Neural Machine Translation](https://mlsanity.com/paper/2101.12462/) (2021)
- [PePe: Personalized Post-editing Model utilizing User-generated Post-edits](https://mlsanity.com/paper/2209.10139/) (2022)
- [Pluggable Neural Machine Translation Models via Memory-augmented Adapters](https://mlsanity.com/paper/2307.06029/) (2023)
- [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)
- [Incorporating Human Translator Style into English-Turkish Literary Machine Translation](https://mlsanity.com/paper/2307.11457/) (2023)

## Abstract (arXiv)

Every person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin. When attempting to perform Machine Translation (MT), these variations have a significant effect on how the system should perform translation, but this is not captured well by standard one-size-fits-all models. In this paper, we propose a simple and parameter-efficient adaptation technique that only requires adapting the bias of the output softmax to each particular user of the MT system, either directly or through a factored approximation. Experiments on TED talks in three languages demonstrate improvements in translation accuracy, and better reflection of speaker traits in the target text.