mlsanityData to 7 Oct 2026

Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Prompts

arXiv 2209.11409 ↗First posted 23 Sep 2022cs.CLcited by 7

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Abstract (arXiv)

Domain adaptation is an important challenge for neural machine translation. However, the traditional fine-tuning solution requires multiple extra training and yields a high cost. In this paper, we propose a non-tuning paradigm, resolving domain adaptation with a prompt-based method. Specifically, we construct a bilingual phrase-level database and retrieve relevant pairs from it as a prompt for the input sentences. By utilizing Retrieved Phrase-level Prompts (RePP), we effectively boost the translation quality. Experiments show that our method improves domain-specific machine translation for 6.2 BLEU scores and improves translation constraints for 11.5% accuracy without additional training.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 28 of its 32 references that are in the map. First posted on arXiv 23 Sep 2022.

Builds on

28 of 32 references.

References · 28

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16 more
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Cited by year

Citing papers per year: 2022: 2; 2023: 3; 2024: 2.

7 papers.

  1. 2023Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translationcited by 74
  2. 2022Controlling Styles in Neural Machine Translation with Activation Promptcited by 7
  3. 2022Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translationcited by 7
  4. 2023Toward Unified Controllable Text Generation via Regular Expression Instructioncited by 7
  5. 2024GrammaMT: Improving Machine Translation with Grammar-Informed In-Context Learningcited by 5
  6. 2024Language Modelling Approaches to Adaptive Machine Translationcited by 3
  7. 2023Unified Model Learning for Various Neural Machine Translationcited by 0