Adaptive Machine Translation with Large Language Models

• URL: http://arxiv.org/abs/2301.13294v3
• Date: Tue, 9 May 2023 11:04:43 GMT
• Title: Adaptive Machine Translation with Large Language Models
• Authors: Yasmin Moslem, Rejwanul Haque, John D. Kelleher, Andy Way
• Abstract summary: We investigate how we can utilize in-context learning to improve real-time adaptive machine translation. We conduct experiments across five diverse language pairs, namely English-to-Arabic (EN-AR), English-to-Chinese (EN-ZH), English-to-French (EN-FR), English-to-Kinyarwanda (EN-RW), and English-to-Spanish (EN-ES)
• Score: 7.803471587734353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Consistency is a key requirement of high-quality translation. It is especially important to adhere to pre-approved terminology and adapt to corrected translations in domain-specific projects. Machine translation (MT) has achieved significant progress in the area of domain adaptation. However, real-time adaptation remains challenging. Large-scale language models (LLMs) have recently shown interesting capabilities of in-context learning, where they learn to replicate certain input-output text generation patterns, without further fine-tuning. By feeding an LLM at inference time with a prompt that consists of a list of translation pairs, it can then simulate the domain and style characteristics. This work aims to investigate how we can utilize in-context learning to improve real-time adaptive MT. Our extensive experiments show promising results at translation time. For example, LLMs can adapt to a set of in-domain sentence pairs and/or terminology while translating a new sentence. We observe that the translation quality with few-shot in-context learning can surpass that of strong encoder-decoder MT systems, especially for high-resource languages. Moreover, we investigate whether we can combine MT from strong encoder-decoder models with fuzzy matches, which can further improve translation quality, especially for less supported languages. We conduct our experiments across five diverse language pairs, namely English-to-Arabic (EN-AR), English-to-Chinese (EN-ZH), English-to-French (EN-FR), English-to-Kinyarwanda (EN-RW), and English-to-Spanish (EN-ES).

Related papers

• Fine-tuning Large Language Models for Domain-specific Machine Translation [8.439661191792897]
  Large language models (LLMs) have made significant progress in machine translation (MT) However, their potential in domain-specific MT remains under-explored. This paper proposes a prompt-oriented fine-tuning method, denoted as LlamaIT, to effectively and efficiently fine-tune a general-purpose LLM for domain-specific MT tasks.
  arXiv  Detail & Related papers  (2024-02-23T02:24:15Z)
• Language Modelling Approaches to Adaptive Machine Translation [0.0]
  Consistency is a key requirement of high-quality translation. In-domain data scarcity is common in translation settings. Can we employ language models to improve the quality of adaptive MT at inference time?
  arXiv  Detail & Related papers  (2024-01-25T23:02:54Z)
• Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions [0.0]
  Generative large language models (LLMs) have demonstrated exceptional proficiency in various natural language processing (NLP) tasks. We conducted an investigation into two popular prompting methods and their combination, focusing on cross-language combinations of Persian, English, and Russian.
  arXiv  Detail & Related papers  (2024-01-16T15:16:34Z)
• Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models [91.6543868677356]
  The evolution of Neural Machine Translation has been influenced by six core challenges. These challenges include domain mismatch, amount of parallel data, rare word prediction, translation of long sentences, attention model as word alignment, and sub-optimal beam search. This study revisits these challenges, offering insights into their ongoing relevance in the context of advanced Large Language Models.
  arXiv  Detail & Related papers  (2024-01-16T13:30:09Z)
• Speech Translation with Large Language Models: An Industrial Practice [64.5419534101104]
  We introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained large language model (LLM) By integrating the large language model (LLM) with a speech encoder and employing multi-task instruction tuning, LLM-ST can produce accurate timestamped transcriptions and translations. Through rigorous experimentation on English and Chinese datasets, we showcase the exceptional performance of LLM-ST.
  arXiv  Detail & Related papers  (2023-12-21T05:32:49Z)
• Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting [36.24578487904221]
  We focus on how to integrate multi-knowledge, multiple types of knowledge, into NMT models to enhance the performance with prompting. We propose a unified framework, which can integrate effectively multiple types of knowledge including sentences, terminologies/phrases and translation templates into NMT models.
  arXiv  Detail & Related papers  (2023-12-08T02:55:00Z)
• Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing [12.843274390224853]
  Large Language Models (LLM's) have demonstrated considerable success in various Natural Language Processing tasks. We show that they have yet to attain state-of-the-art performance in Neural Machine Translation. We propose adapting LLM's as Automatic Post-Editors (APE) rather than direct translators.
  arXiv  Detail & Related papers  (2023-10-23T12:22:15Z)
• Towards Effective Disambiguation for Machine Translation with Large Language Models [65.80775710657672]
  We study the capabilities of large language models to translate "ambiguous sentences" Experiments show that our methods can match or outperform state-of-the-art systems such as DeepL and NLLB in four out of five language directions.
  arXiv  Detail & Related papers  (2023-09-20T22:22:52Z)
• TIM: Teaching Large Language Models to Translate with Comparison [78.66926087162672]
  We propose a novel framework using examples in comparison to teach LLMs to learn translation. Our approach involves presenting the model with examples of correct and incorrect translations and using a preference loss to guide the model's learning. Our findings offer a new perspective on fine-tuning LLMs for translation tasks and provide a promising solution for generating high-quality translations.
  arXiv  Detail & Related papers  (2023-07-10T08:15:40Z)
• ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback [90.20262941911027]
  ParroT is a framework to enhance and regulate the translation abilities during chat. Specifically, ParroT reformulates translation data into the instruction-following style. We propose three instruction types for finetuning ParroT models, including translation instruction, contrastive instruction, and error-guided instruction.
  arXiv  Detail & Related papers  (2023-04-05T13:12:00Z)
• Bootstrapping a Crosslingual Semantic Parser [74.99223099702157]
  We adapt a semantic trained on a single language, such as English, to new languages and multiple domains with minimal annotation. We query if machine translation is an adequate substitute for training data, and extend this to investigate bootstrapping using joint training with English, paraphrasing, and multilingual pre-trained models.
  arXiv  Detail & Related papers  (2020-04-06T12:05:02Z)

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.