Leveraging Large Language Models for Iterative Translation Refinement and Reduced Translationese

Core Concepts

Iterative translation refinement with large language models can effectively reduce "translationese" in the output, achieving comparable or improved translation quality compared to initial machine translations and even human references.

Abstract

The paper proposes an iterative translation refinement method that leverages the power of large language models (LLMs) like GPT-3.5 to produce more natural and fluent translations. The key insights are: Iterative refinement: The authors prompt the LLM to refine the initial translation in multiple rounds, allowing the model to rewrite the translation from scratch rather than just fixing errors. Anchoring to source and initial translation: The refinement process is anchored to both the source input and the initial translation, ensuring the refined output maintains quality and relevance. Leveraging target-side language modeling: LLMs have seen orders of magnitude more target-side data than typical translation or post-editing datasets, enabling them to generate more natural target language. Experiments on high-resource language pairs (EN-DE, EN-ZH) and low/medium-resource pairs (EN-JA, DE-FR, SAH-RU, UK-CS) show that the refined translations achieve comparable or higher neural metric scores compared to initial LLM translations, despite significant drops in string-based metrics like BLEU. Human evaluations further demonstrate that the refined outputs are preferred over both initial LLM translations and human references in terms of reduced "translationese" - unnatural language due to source interference and the translation process. The authors also investigate different refinement strategies, finding that starting with a reasonable initial translation and anchoring the process to the source input are crucial for obtaining high-quality results. Paraphrasing without the source input, on the other hand, leads to semantic drift. Overall, the paper presents a simple yet effective method to leverage the strengths of LLMs for more natural and fluent translation, going beyond just fixing errors.

Stats

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Quotes

"Our method offers two strengths for combating translationese: 1) LLM prompting allows for iterative and arbitrary re-writing compared to APE which is limited to error fixing without style improvement (Ive et al., 2020); 2) incorporating natural language data leads to more natural translations (Sennrich et al., 2016; Freitag et al., 2019), and LLMs have seen target-side data orders of magnitude larger than datasets for translation or post-editing."

Key Insights Distilled From

Iterative Translation Refinement with Large Language Models

by Pinzhen Chen... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2306.03856.pdf

Deeper Inquiries

How can the iterative refinement process be further improved or automated to make it more efficient and scalable?

The iterative refinement process can be enhanced by incorporating reinforcement learning techniques to guide the refinement process based on feedback received during each iteration. By training a model to learn from the changes made in each round of refinement and adjusting its approach accordingly, the process can become more efficient and effective over time. Additionally, implementing a mechanism to automatically determine the number of iterations needed based on the complexity of the input text and the desired level of refinement can make the process more scalable. This adaptive approach would ensure that the refinement process is tailored to the specific characteristics of each translation task, optimizing both efficiency and scalability.

What other techniques could be combined with the iterative refinement approach to address specific types of translationese or improve translation quality in different domains?

To address specific types of translationese or improve translation quality in different domains, techniques such as domain adaptation and style transfer could be integrated with the iterative refinement approach. Domain adaptation techniques can help fine-tune the language model used in the refinement process to better suit the specific domain of the text being translated, improving the accuracy and naturalness of the output. Additionally, incorporating style transfer methods can help adjust the tone, formality, or register of the translation to better match the desired style, reducing instances of translationese and enhancing overall quality. By combining these techniques with iterative refinement, the system can effectively target and mitigate specific translation issues prevalent in different domains.

Given the potential of LLMs for natural language generation, how might this technology be leveraged to assist human translators or enhance the overall translation ecosystem?

LLMs can be leveraged to assist human translators by providing real-time suggestions, corrections, and alternative translations during the translation process. By integrating LLMs into translation tools or platforms, human translators can benefit from the model's vast knowledge and language capabilities to improve the accuracy and fluency of their translations. LLMs can also be used for pre-translation tasks, where they generate initial drafts that human translators can then refine and edit, speeding up the translation process while maintaining high quality. Furthermore, LLMs can be employed for post-editing tasks to automatically refine and enhance translations, reducing the need for extensive manual editing. Overall, integrating LLMs into the translation workflow can streamline the process, improve translation quality, and enhance the overall efficiency of the translation ecosystem.