---
title: "Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization"
authors:
  - "Yuhan Wu"
  - "Huan Zhang"
  - "Wei Cheng"
  - "Chen Shen"
  - "Jingyue Yang"
  - "Wei Hu"
doi: "10.24963/ijcai.2026/665"
url: "https://ijcai.org/proceedings/2026/665"
pdf: "https://ijcai.org/proceedings/2026/0665.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "5973-5981"
track: "Main Track"
keywords:
  - "Natural Language Processing: Machine translation and multilinguality"
  - "Natural Language Processing: Applications"
---

# Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization

Yuhan Wu, Huan Zhang, Wei Cheng, Chen Shen, Jingyue Yang, Wei Hu

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 5973-5981. DOI: [10.24963/ijcai.2026/665](https://doi.org/10.24963/ijcai.2026/665)

## Abstract

LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While preference-based learning offers a promising alignment strategy, it is hindered by unreliable semantic rewards derived from sparse test cases or restrictive reference translations. We argue that a robust semantic reward for code translation must be derived directly from the source code. In this paper, we propose CTO to improve code translation with syntax-guided and semantic-aware preference optimization. 
Through contrastive learning, we train a cross-lingual semantic model to directly assess functional equivalence between source and translated code. By formulating code translation as a multi-objective optimization problem, this robust semantic signal is seamlessly unified with compiler-based syntactic feedback within the direct preference optimization framework. Extensive experiments on C++, Java, and Python translations demonstrate that CTO significantly outperforms existing baselines and alternative preference optimization strategies.