title: New Trends for Modern Machine Translation with Large Reasoning Models
summary: Recent advances in Large Reasoning Models (LRMs), particularly those
  leveraging Chain-of-Thought reasoning (CoT), have opened brand new possibility
  for Machine Translation (MT). This position paper argues that LRMs
  substantially transformed traditional neural MT as well as LLMs-based MT
  paradigms by reframing translation as a dynamic reasoning task that requires
  contextual, cultural, and linguistic understanding and reasoning. We identify
  three foundational shifts: 1) contextual coherence, where LRMs resolve
  ambiguities and preserve discourse structure through explicit reasoning over
  cross-sentence and complex context or even lack of context; 2) cultural
  intentionality, enabling models to adapt outputs by inferring speaker intent,
  audience expectations, and socio-linguistic norms; 3) self-reflection, LRMs can
  perform self-reflection during the inference time to correct the potential
  errors in translation especially extremely noisy cases, showing better
  robustness compared to simply mapping X->Y translation. We explore various
  scenarios in translation including stylized translation, document-level
  translation and multimodal translation by showcasing empirical examples that
  demonstrate the superiority of LRMs in translation. We also identify several
  interesting phenomenons for LRMs for MT including auto-pivot translation as
  well as the critical challenges such as over-localisation in translation and
  inference efficiency. In conclusion, we think that LRMs redefine translation
  systems not merely as text converters but as multilingual cognitive agents
  capable of reasoning about meaning beyond the text. This paradigm shift reminds
  us to think of problems in translation beyond traditional translation scenarios
  in a much broader context with LRMs - what we can achieve on top of it.
paper_authors._id: 67d39b35acb72b994659d4fd
paper_authors.name: Sinuo Liu
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paper_authors.fullname: LiuSinuo
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paper_authors._id: 67d39b35acb72b994659d4fe
paper_authors.name: Chenyang Lyu
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paper_authors.fullname: Chenyang Lyu
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paper_authors.name: Minghao Wu
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paper_authors.fullname: Minghao Wu
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paper_authors.name: Longyue Wang
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paper_authors.fullname: Longyue Wang
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paper_authors.name: Weihua Luo
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paper_authors.fullname: Weihua Luo
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paper_authors.user: acecamel1977
paper_authors._id: 67d39b35acb72b994659d502
paper_authors.name: Kaifu Zhang
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paper_authors.fullname: Kaifu Zhang
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paper_authors.user: zhangkaifu314
paper_title: New Trends for Modern Machine Translation with Large Reasoning Models
paper_summary: Recent advances in Large Reasoning Models (LRMs), particularly those
  leveraging Chain-of-Thought reasoning (CoT), have opened brand new possibility
  for Machine Translation (MT). This position paper argues that LRMs
  substantially transformed traditional neural MT as well as LLMs-based MT
  paradigms by reframing translation as a dynamic reasoning task that requires
  contextual, cultural, and linguistic understanding and reasoning. We identify
  three foundational shifts: 1) contextual coherence, where LRMs resolve
  ambiguities and preserve discourse structure through explicit reasoning over
  cross-sentence and complex context or even lack of context; 2) cultural
  intentionality, enabling models to adapt outputs by inferring speaker intent,
  audience expectations, and socio-linguistic norms; 3) self-reflection, LRMs can
  perform self-reflection during the inference time to correct the potential
  errors in translation especially extremely noisy cases, showing better
  robustness compared to simply mapping X->Y translation. We explore various
  scenarios in translation including stylized translation, document-level
  translation and multimodal translation by showcasing empirical examples that
  demonstrate the superiority of LRMs in translation. We also identify several
  interesting phenomenons for LRMs for MT including auto-pivot translation as
  well as the critical challenges such as over-localisation in translation and
  inference efficiency. In conclusion, we think that LRMs redefine translation
  systems not merely as text converters but as multilingual cognitive agents
  capable of reasoning about meaning beyond the text. This paradigm shift reminds
  us to think of problems in translation beyond traditional translation scenarios
  in a much broader context with LRMs - what we can achieve on top of it.
paper_ai_keywords: Chain-of-Thought reasoning (CoT) Large Reasoning Models (LRMs) Neural MT Contextual coherence Cultural intentionality Self-reflection Stylized translation Document-level translation Multimodal translation Auto-pivot translation Over-localisation Inference efficiency Multilingual cognitive agents