New Trends for Modern Machine Translation with Large Reasoning Models
Authors: name: Sinuo Liu, name: Chenyang Lyu, name: Minghao Wu, name: Longyue Wang, name: Weihua Luo, name: Kaifu Zhang

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.