title: DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
summary: Recently, O1-like models have emerged as representative examples,
  illustrating the effectiveness of long chain-of-thought (CoT) in reasoning
  tasks such as math and coding tasks. In this paper, we introduce DRT-o1, an
  attempt to bring the success of long CoT to neural machine translation (MT).
  Specifically, in view of the literature books that might involve similes and
  metaphors, translating these texts to a target language is very difficult in
  practice due to cultural differences. In such cases, literal translation often
  fails to convey the intended meaning effectively. Even for professional human
  translators, considerable thought must be given to preserving semantics
  throughout the translation process. To simulate LLMs' long thought ability in
  MT, we first mine sentences containing similes or metaphors from existing
  literature books, and then develop a multi-agent framework to translate these
  sentences via long thought. In the multi-agent framework, a translator is used
  to iteratively translate the source sentence under the suggestions provided by
  an advisor. To ensure the effectiveness of the long thoughts, an evaluator is
  also employed to judge whether the translation in the current round is better
  than the previous one or not. In this manner, we collect tens of thousands of
  long-thought MT data, which is used to train our DRT-o1. The experimental
  results on literature translation demonstrate the effectiveness of the DRT-o1.
  Using Qwen2.5-7B and Qwen2.5-14B as the backbones, the improvement brought by
  DRT-o1 achieves 7.33~8.26 BLEU and 1.66~3.36 CometScore. Besides, DRT-o1-7B can
  outperform QwQ-32B-Preview by 7.82 BLEU and 1.46 CometScore, showing its
  effectiveness. The project is available at https://github.com/krystalan/DRT-o1
paper_authors._id: 676a1e8facedf3baab442be0
paper_authors.name: Jiaan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-30T19:35:48.135Z
paper_authors._id: 6051e3f145db307eddc0c962
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1676443438507-6051e3f145db307eddc0c962.jpeg
paper_authors.fullname: Jiaan Wang
paper_authors.type: user
paper_authors.user: Krystalan
paper_authors._id: 676a1e8facedf3baab442be1
paper_authors.name: Fandong Meng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-05T23:03:09.771Z
paper_authors._id: 64cb254871a7bbb60c17d5fa
paper_authors.avatarUrl: /avatars/5121fd5b7b55d275eba3947f3f4c034d.svg
paper_authors.fullname: Fandong Meng
paper_authors.type: user
paper_authors.user: fandong
paper_authors._id: 676a1e8facedf3baab442be2
paper_authors.name: Yunlong Liang
paper_authors._id: 676a1e8facedf3baab442be3
paper_authors.name: Jie Zhou
paper_title: DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
paper_summary: Recently, O1-like models have emerged as representative examples,
  illustrating the effectiveness of long chain-of-thought (CoT) in reasoning
  tasks such as math and coding tasks. In this paper, we introduce DRT-o1, an
  attempt to bring the success of long CoT to neural machine translation (MT).
  Specifically, in view of the literature books that might involve similes and
  metaphors, translating these texts to a target language is very difficult in
  practice due to cultural differences. In such cases, literal translation often
  fails to convey the intended meaning effectively. Even for professional human
  translators, considerable thought must be given to preserving semantics
  throughout the translation process. To simulate LLMs' long thought ability in
  MT, we first mine sentences containing similes or metaphors from existing
  literature books, and then develop a multi-agent framework to translate these
  sentences via long thought. In the multi-agent framework, a translator is used
  to iteratively translate the source sentence under the suggestions provided by
  an advisor. To ensure the effectiveness of the long thoughts, an evaluator is
  also employed to judge whether the translation in the current round is better
  than the previous one or not. In this manner, we collect tens of thousands of
  long-thought MT data, which is used to train our DRT-o1. The experimental
  results on literature translation demonstrate the effectiveness of the DRT-o1.
  Using Qwen2.5-7B and Qwen2.5-14B as the backbones, the improvement brought by
  DRT-o1 achieves 7.33~8.26 BLEU and 1.66~3.36 CometScore. Besides, DRT-o1-7B can
  outperform QwQ-32B-Preview by 7.82 BLEU and 1.46 CometScore, showing its
  effectiveness. The project is available at https://github.com/krystalan/DRT-o1
paper_ai_summary: DRT-o1, a neural machine translation model using a multi-agent framework, improves translation quality by simulating long chain-of-thought reasoning, particularly in handling texts with similes and metaphors.
paper_ai_keywords: long chain-of-thought neural machine translation multi-agent framework similes metaphors translation BLEU CometScore