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Large Language Models for Interactive Machine Translation
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AI
DOI：10.1007/978-3-031-99568-2_3.png)
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Abstract
En 中文
Machine translation is an ever-evolving field and is attached to continuous improvement. Consequently, the results that it offers are far from being perfect but are obtained really fast. In practical applications, the translations obtained automatically need to be revised by a human translator. On the other hand, the translations performed completely by humans have a high quality but take much time. Interactive Machine Translation (IMT) has been one of the most promising approaches to improve the quality of the translation while minimizing the user effort and time needed. Last advances in natural language processing have involved Large Language Models (LLM) with great success. In this work we integrate LLMs into two IMT interaction protocols: prefix-based and segment-based. We have performed a comparative study with four different multilingual LLMs for the IMT task in a renowned dataset for both IMT protocols. The systems that we propose effectively reduce the post-editing effort for the prefix-based approach.
Keywords:
Language model
machine translation
interactive machine translation
segment-based interactive machine translation
restricted generation
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Papers:
26
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