DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
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
676a1e8facedf3baab442be0
Jiaan Wang
claimed_verified
2024-12-30T19:35:48.135Z
6051e3f145db307eddc0c962
https://cdn-avatars.huggingface.co/v1/production/uploads/1676443438507-6051e3f145db307eddc0c962.jpeg
Jiaan Wang
user
Krystalan
676a1e8facedf3baab442be1
Fandong Meng
claimed_verified
2025-01-05T23:03:09.771Z
64cb254871a7bbb60c17d5fa
/avatars/5121fd5b7b55d275eba3947f3f4c034d.svg
Fandong Meng
user
fandong
676a1e8facedf3baab442be2
Yunlong Liang
676a1e8facedf3baab442be3
Jie Zhou
DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
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
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.
long chain-of-thought neural machine translation multi-agent framework similes metaphors translation BLEU CometScore