mlsanityData to 14 Oct 2026

Extreme Adaptation for Personalized Neural Machine Translation

arXiv 1805.01817 ↗First posted 4 May 2018cs.CLcited by 27

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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.

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

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Citing papers per year: 2021: 8; 2022: 6; 2023: 10; 2024: 2; 2025: 1.

27 papers.

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  1. 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,245
  2. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 321
  3. 2023Tool Learning with Foundation Modelscited by 302
  4. 2023Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedbackcited by 87
  5. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  6. 2022CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formalitycited by 24
  7. 2022On the Domain Adaptation and Generalization of Pretrained Language Models: A Surveycited by 23
  8. 2021Machine Translation Verbosity Control for Automatic Dubbingcited by 18
  9. 2022FRMT: A Benchmark for Few-Shot Region-Aware Machine Translationcited by 18
  10. 2021Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translationcited by 15
  11. 2024What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Studycited by 15
  12. 2021Towards User-Driven Neural Machine Translationcited by 10
15 more
  1. 2023RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translationcited by 9
  2. 2021Towards Universality in Multilingual Text Rewritingcited by 7
  3. 2022Controlling Styles in Neural Machine Translation with Activation Promptcited by 7
  4. 2023Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillationcited by 4
  5. 2022Controlling Extra-Textual Attributes about Dialogue Participants -- A Case Study of English-to-Polish Neural Machine Translationcited by 3
  6. 2023Reference-less Analysis of Context Specificity in Translation with Personalised Language Modelscited by 3
  7. 2025Steering Large Language Models for Machine Translation Personalizationcited by 3
  8. 2023MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translationcited by 2
  9. 2023How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models?cited by 2
  10. 2021Synthesizing Monolingual Data for Neural Machine Translationcited by 1
  11. 2022PePe: Personalized Post-editing Model utilizing User-generated Post-editscited by 1
  12. 2023Pluggable Neural Machine Translation Models via Memory-augmented Adapterscited by 1
  13. 2024Diverse Sign Language Translationcited by 1
  14. 2023Unified Model Learning for Various Neural Machine Translationcited by 0
  15. 2023Incorporating Human Translator Style into English-Turkish Literary Machine Translationcited by 0