A Reinforcement Learning Framework for Cross-Lingual Stance Detection Using Chain-of-Thought Alignment

Abstract

Cross-lingual stance detection identifies users’ attitudes toward specific targets in texts by transferring knowledge from source languages to target languages. Previous studies have typically facilitated this transfer by translating and aligning labels or targets. However, these methods cannot effectively perform cross-lingual transfer of the complex reasoning processes in stance detection. To address this challenge, we propose a reinforcement learning framework using cross-lingual Chain-of-Thought (CoT) alignment, referred to as RCCA. Specifically, we adopt a cross-lingual CoT alignment strategy to obtain the high-quality CoTs generated from target language inputs. After that, we leverage reinforcement learning by sampling CoTs and assigning rewards according to predefined rules, aiming to enhance the model’s generalization capabilities in the target language. Experimental results on four multilingual datasets demonstrate that our approach outperforms competitive methods.