A Reinforcement Learning Framework for Cross-Lingual Stance Detection Using Chain-of-Thought Alignment (2025)

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.

## introduction stance detection automatically identifies users' attitudes (e.g., "in favor of" or "against") toward specific targets, which is crucial for text mining and social media analytics @xcite . compared to the widely available english corpora used in research @xcite @xcite , most other languages often lack sufficient training data. to address the issue of imbalanced data resources among languages and support the application of stance detection in low-resource languages, a cross-lingual stance detection task is proposed to transfer stance detection capabilities from source language to target language @xcite . figure 1 presents an example of cross-lingual stance detection in which the model transfers knowledge from an english dataset to a french dataset. * corresponding author previous studies have typically facilitated crosslingual adaptation through several approaches: (1) label-based alignment: mohtarami et al. (2019) introduce a contrastive stance alignment-based adaptation method, aligning class labels across languages. (2) target-based alignment: zhang et al. (2023b) resolve the target inconsistency problem by constructing intra-lingual and cross-lingual target relationship graphs and designing a target alignment strategy. (3) integrated label and target alignment: zhang et al. (2023c) proposes a dual distillation framework for cross-lingual and crosstarget scenarios. the cross-lingual teacher model is trained using prompt tuning and label alignment methods, while the cross-target teacher model bridge the gap of target inconsistency through target representation learning and contrastive learning. however, these methods cannot effectively perform cross-lingual transfer of the complex reasoning processes in stance detection. on the one hand, the lack of sufficient training data in target languages makes it difficult for the model to generate accurate reasoning. on the other hand, the complex nature of stance detection reasoning, along with the differences in expression patterns and socio-cultural backgrounds among different languages, prevents the reasoning ability acquired in the source language from being effectively generalized in the target language. to overcome this challenge, we propose a reinforcement learning framework using cross-lingual cot @xcite alignment with large language models (llms). in the cross-lingual cot alignment phase, we use well-designed prompts for the stance detection task to guide the model in generating clear reasoning processes. we employ a cot alignment strategy to obtain high-quality cots from target language inputs, effectively alleviating the issue of insufficient training data for target languages. in the reinforcement learning phase, we first use the obtained cots for initial supervised fine-tuning (sft) to equip the model with basic stance detection capabilities. subsequently, through reinforcement learning, we enable the model to better adapt to the unique expressive characteristics of the target language, maintaining reasoning abilities consistent with the source language while enhancing generalization capabilities in the target language. our main contributions are summarized as follows: @xcite we propose a reinforcement learning framework using cross-lingual cot alignment, which effectively enables cross-lingual transfer of complex reasoning processes in stance detection tasks. (2) the cross-lingual cot alignment strategy addresses the issue of scarce high-quality training data in the target language, while reinforcement learning enhances the model's generalization ability for reasoning stances in cross-lingual scenarios. ## method figure 2 illustrates the overall structure of the proposed cross-lingual stance detection method, rcca. in the cross-lingual cot alignment phase, we designed prompts tailored to the stance detection task, leveraging the cots generated by the model in the source language to guide the generation of correct cots in the target language. in the reinforcement learning phase, we enhanced the model's reasoning ability in target language scenarios through cot sampling and a rule-based reward mechanism. we denote the set of training data in the source language as d @xmath0, x i ), y i )} n @xmath1, where t i , x i and y i represent the target, text, and stance label for each instance, respectively, with labels being favor, against, or neutral. similarly, we denote the translated training data in the target language as d @xmath2, x ′ i ), y i )} n @xmath3, where t ′ i and x ′ i are the translations of t i and x i . ## conclusion in this work, we present a reinforcement learning framework using cross-lingual cot alignment (rcca), designed to effectively transfer the complex reasoning processes in cross-lingual stance detection. experiments show that our method outperforms other methods in cross-lingual stance detection task. furthermore, rcca has strong potential for broader application to cross-lingual tasks beyond stance detection and can effectively address challenges in low-resource languages.