Iterative Translation Refinement with Large Language Models

• URL: http://arxiv.org/abs/2306.03856v2
• Date: Wed, 1 May 2024 20:44:01 GMT
• Title: Iterative Translation Refinement with Large Language Models
• Authors: Pinzhen Chen, Zhicheng Guo, Barry Haddow, Kenneth Heafield,
• Abstract summary: We propose iteratively prompting a large language model to self-correct a translation. We also discuss the challenges in evaluation and relation to human performance and translationese.
• Score: 25.90607157524168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose iteratively prompting a large language model to self-correct a translation, with inspiration from their strong language understanding and translation capability as well as a human-like translation approach. Interestingly, multi-turn querying reduces the output's string-based metric scores, but neural metrics suggest comparable or improved quality. Human evaluations indicate better fluency and naturalness compared to initial translations and even human references, all while maintaining quality. Ablation studies underscore the importance of anchoring the refinement to the source and a reasonable seed translation for quality considerations. We also discuss the challenges in evaluation and relation to human performance and translationese.

Related papers

• BiVert: Bidirectional Vocabulary Evaluation using Relations for Machine Translation [4.651581292181871]
  We propose a bidirectional semantic-based evaluation method designed to assess the sense distance of the translation from the source text. This approach employs the comprehensive multilingual encyclopedic dictionary BabelNet. Factual analysis shows a strong correlation between the average evaluation scores generated by our method and the human assessments across various machine translation systems for English-German language pair.
  arXiv  Detail & Related papers  (2024-03-06T08:02:21Z)
• Advancing Translation Preference Modeling with RLHF: A Step Towards Cost-Effective Solution [57.42593422091653]
  We explore leveraging reinforcement learning with human feedback to improve translation quality. A reward model with strong language capabilities can more sensitively learn the subtle differences in translation quality.
  arXiv  Detail & Related papers  (2024-02-18T09:51:49Z)
• Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model [75.66013048128302]
  In this work, we investigate the potential of employing the QE model as the reward model to predict human preferences for feedback training. We first identify the overoptimization problem during QE-based feedback training, manifested as an increase in reward while translation quality declines. To address the problem, we adopt a simple yet effective method that uses rules to detect the incorrect translations and assigns a penalty term to the reward scores of them.
  arXiv  Detail & Related papers  (2024-01-23T16:07:43Z)
• The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning [50.320178219081484]
  We propose an active learning approach that exploits the strengths of both human and machine translations. An ideal utterance selection can significantly reduce the error and bias in the translated data.
  arXiv  Detail & Related papers  (2023-05-22T05:57:47Z)
• Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality? [61.866103154161884]
  Neural machine translation (NMT) is often criticized for failures that happen without awareness. We propose a novel competency-aware NMT by extending conventional NMT with a self-estimator. We show that the proposed method delivers outstanding performance on quality estimation.
  arXiv  Detail & Related papers  (2022-11-25T02:39:41Z)
• Consistent Human Evaluation of Machine Translation across Language Pairs [21.81895199744468]
  We propose a new metric called XSTS that is more focused on semantic equivalence and a cross-lingual calibration method. We demonstrate the effectiveness of these novel contributions in large scale evaluation studies across up to 14 language pairs.
  arXiv  Detail & Related papers  (2022-05-17T17:57:06Z)
• DEEP: DEnoising Entity Pre-training for Neural Machine Translation [123.6686940355937]
  It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. We propose DEEP, a DEnoising Entity Pre-training method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences.
  arXiv  Detail & Related papers  (2021-11-14T17:28:09Z)
• A Set of Recommendations for Assessing Human-Machine Parity in Language Translation [87.72302201375847]
  We reassess Hassan et al.'s investigation into Chinese to English news translation. We show that the professional human translations contained significantly fewer errors.
  arXiv  Detail & Related papers  (2020-04-03T17:49:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.