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Enhancing Machine Translation Quality through Large Language Model-based Post-Editing with Error Annotations

Core Concepts
Leveraging the complementary strengths of large language models (LLMs) and supervised machine translation (MT) systems, this work explores strategies to guide LLaMA-2 models to improve MT outputs using external feedback on translation errors.
The paper explores techniques to guide LLaMA-2 language models to improve machine translation (MT) outputs using external feedback on translation errors. The authors consider three levels of feedback granularity: generic, score-based, and fine-grained error annotations. For the prompting experiments: Zero-shot prompting with any form of feedback leads to marginal improvements in translation quality metrics like BLEU, TER, and COMET. 10-shot prompting widens the performance gap between the original and post-edited MT, with consistent gains in BLEU, TER, and COMET scores. The performance gap between the smaller 7B and larger 13B LLaMA-2 models narrows down with increased few-shot examples, suggesting that few-shot learning helps bridge the size gap for MT post-editing. The different granularity of feedback show similar performance in the 10-shot setting, with the fine-grained feedback not providing a clear advantage over generic feedback. For the fine-tuning experiments: Fine-tuning the LLaMA-2 models with error-annotated translations leads to significant improvements in translation quality over the original MT, outperforming the best prompting results. The multilingual fine-tuning approach, which combines three language pairs, generally outperforms the bilingual fine-tuning. Human evaluation confirms that fine-tuning not only resolves the targeted errors but also generates more natural translations in the target language. The analysis reveals that fine-tuning helps the LLMs effectively integrate the provided fine-grained feedback to address the specific errors in the initial translation.
The translation quality of the original MT outputs has an average BLEU score of 0.45, TER of 0.81, and COMET score of 0.71 across the three language pairs. The zero-shot prompting with any form of feedback leads to a marginal improvement of around 0.01-0.02 BLEU, 0.03-0.06 TER, and 0.01-0.02 COMET. The 10-shot prompting achieves an average improvement of 0.04 BLEU, 0.03 COMET, and 0.04 TER over the original MT. The fine-tuned models show an average improvement of 0.07 BLEU, 0.08 COMET, and 0.21 TER over the original MT.
"Leveraging the complementary strengths of LLMs and supervised MT by guiding LLMs to automatically post-edit MT with external feedback on its quality, derived from Multidimensional Quality Metric (MQM) annotations." "Fine-tuning helps integrate fine-grained feedback more effectively and further improves translation quality based on both automatic and human evaluation."

Deeper Inquiries

How can the proposed post-editing workflow be extended to automatically determine whether and how to post-edit any given MT input, possibly selecting among different potential feedback mechanisms?

The proposed post-editing workflow can be extended by incorporating an automated system that can analyze the MT input and determine whether post-editing is necessary. This system can utilize various techniques such as natural language processing (NLP) models to identify errors, inconsistencies, or areas for improvement in the translation. By integrating machine learning algorithms, the system can compare the MT output with the source text, identify potential errors or areas of concern, and then decide on the appropriate feedback mechanism to apply for post-editing. This automated process can involve selecting among different potential feedback mechanisms based on the nature of the errors detected, such as generic feedback, score-based feedback, or fine-grained error annotations. By leveraging advanced NLP technologies and machine learning algorithms, the system can streamline the post-editing process and ensure that the MT output is refined effectively.

How well do the findings generalize to a wider variety of languages, particularly in low-resource settings?

While the findings of the study demonstrate the effectiveness of post-editing MT outputs with external feedback in improving translation quality, the generalizability of these findings to a wider variety of languages, especially in low-resource settings, may vary. The performance of the post-editing workflow in different languages and resource-constrained environments can be influenced by several factors such as the availability of training data, the complexity of the language structures, and the quality of the external feedback mechanisms. In low-resource settings, where training data may be limited, the effectiveness of the post-editing workflow could be impacted. Additionally, the linguistic diversity and unique characteristics of various languages may require adaptations or modifications to the workflow to ensure optimal performance. Further research and experimentation in diverse language pairs and low-resource settings are essential to assess the scalability and applicability of the proposed post-editing approach.

What approaches can be developed to generate high-quality, consistent error annotations at scale to address the scarcity of such feedback for MT post-editing?

To address the scarcity of high-quality, consistent error annotations for MT post-editing at scale, several approaches can be developed: Automated Error Annotation Systems: Develop automated systems that can analyze MT outputs and generate error annotations based on predefined criteria or linguistic rules. These systems can leverage NLP techniques and machine learning algorithms to identify errors, categorize them, and provide detailed feedback for post-editing. Crowdsourcing and Human Evaluation: Implement crowdsourcing platforms or human evaluation processes to collect error annotations from linguists, translators, or bilingual speakers. By aggregating feedback from multiple human annotators, it is possible to generate high-quality error annotations at scale. Active Learning Strategies: Utilize active learning strategies to iteratively improve error annotation models. By selecting the most informative instances for manual annotation, active learning can optimize the annotation process and enhance the quality of feedback provided for post-editing. Transfer Learning and Multitask Learning: Apply transfer learning and multitask learning techniques to leverage pre-existing error annotation datasets or related tasks to improve the accuracy and consistency of error annotations. By transferring knowledge from one task to another, these approaches can enhance the scalability and efficiency of error annotation generation. By combining these approaches and leveraging advanced technologies in NLP and machine learning, it is possible to develop robust error annotation systems that can provide high-quality, consistent feedback for MT post-editing at scale.