Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning
Neural machine translation (NMT) in the legal domain is a linguistically and conceptually demanding task, primarily due to the complexity of legal language and the high level of precision it requires. The recent emergence of reasoning-capable language models opens new possibilities for tackling such challenges. They add to a set of other previously proposed techniques to enhance the translation quality, which includes supervised fine-tuning and reinforcement learning. In this work, we perform a comparison between these various approaches. More particularly, we evaluate small language models
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- PossiblePossibly related (embedding) · 48%Ontology Reasoning AI: OWL Logic Meets Large Language Models - AI CERTs →
- LinkedLinked via arxiv author · 85%Aixiu An →
“Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning”
- LinkedLinked via arxiv author · 85%Michael Jungo →
“Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning”
- LinkedLinked via arxiv author · 85%Eloi Eynard →
“Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning”
- LinkedLinked via arxiv author · 85%Mark Drenhaus →
“Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning”
- LinkedLinked via arxiv author · 85%Andreas Fischer →
“Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning”
- LinkedLinked via arxiv author · 85%Jean Hennebert →
“Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning”
- LinkedLinked via arxiv author · 85%Sébastien Rumley →
“Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning”