Target-Oriented Relation Alignment for Cross-Lingual Stance Detection (2023)

Abstract: Stance detection is an important task in text mining and social media analytics, aiming to automatically identify the user’s attitude toward a specific target from text, and has wide applications in a variety of domains. Previous work on stance detection has mainly focused on monolingual setting. To address the problem of imbalanced language resources, cross-lingual stance detection is proposed to transfer the knowledge learned from a high-resource (source) language (typically English) to another low-resource (target) language. However, existing research on cross-lingual stance detection has ignored the inconsistency in the occurrences and distributions of targets between languages, which consequently degrades the performance of stance detection in low-resource languages. In this paper, we first identify the target inconsistency issue in cross-lingual stance detection, and propose a fine-grained Target-oriented Relation Alignment (TaRA) method for the task, which considers both target-level associations and language-level alignments. Specifically, we propose the Target Relation Graph to learn the in-language and cross-language target associations. We further devise the relation alignment strategy to enable knowledge transfer between semantically correlated targets across languages. Experimental results on the representative datasets demonstrate the effectiveness of our method compared to competitive methods under variant settings.

## introduction stance detection is an important task in public opinion mining and social media analytics, which aims to automatically identify the user's attitude (e.g., "in favor of " or "against") toward a specific target (e.g., entity, topic, or claim) from text. it has been widely applied to many domains such as veracity checking, market analysis, social security and gov-text i'm a feminist, i believe in equality for all. #equali-tyforall c'est tellement génial que #lovewinsétende maintenant l'égalité des droits des femmes (it's so awesome that #lovewins -now extends women's equal rights) stance favor favor the original french text with the target is presented with english translation. in addition to the problem of imbalanced language resources, another important issue in crosslingual stance detection has been ignored by current research. due to the differences in contextual information, language expressions and socio-cultural backgrounds, the occurrences and distributions of the concerned targets may vary considerably across languages. for example, even in the same domain such as "presidential election", the targets in the english corpus are distinct from those in the french one. since the discrepancy in target distri-butions pervasively exists between languages, the targets in the source and target language datasets cannot precisely align in cross-lingual stance detection. this brings about the target inconsistency issue, that is, the difficulty of knowledge transfer across languages caused by the misalignment, which inevitably leads to the performance decrease for cross-lingual stance detection. to address the target inconsistency issue, semantic associations between different targets can be utilized for cross-lingual stance detection. in this paper, we model the target-level associations and propose a fine-grained target-oriented relation alignment (tara) method for crosslingual stance detection. our method considers both target-level associations and language-level alignments. in addition, to guarantee the crosslanguage performance on stance detection, the in-language target relation learning and relation contrastive alignments should be maintained first. specifically, it first learns the relations between different targets via target relation graph within each language (i.e., in-language), and constructs the cross-lingual relation graph to compensate for target inconsistency. we then devise the in-language and cross-language relation alignment strategies to align the samples with highly correlated targets based on the relation graph, so as to enable knowledge transfer between semantically correlated targets across languages. the contributions of our work are as follows: ## related work stance detection has been well studied on english datasets @xcite @xcite @xcite . previous methods for stance detection mainly focus on monolingual setting to learn a supervised classification model with labeled data. the mainstream research centers on stance detection for pre-defined targets @xcite @xcite @xcite , cross-target stance detection @xcite @xcite and few/zero-shot stance detection @xcite . compared to the abundant resources in english, there are much fewer data resources in other languages @xcite @xcite @xcite . to promote stance detection in low-resource languages, some researchers make efforts to construct multilingual stance datasets @xcite @xcite , while other research develops the methods for cross-lingual stance detection @xcite . @xcite conducts a comprehensive empirical study on pretraining the language model with additional corpora in the source language, to acquire and transfer the knowledge to the target language through prompt-tuning. @xcite proposes a contrastive language adaptation method to align the representations across languages, which encourages samples with the same label in different languages to be closer in the embedding space. however, their method only considers the contrastive adaptation at the language level, ignoring the finegrained modeling of target relations that is essential to the compensation for target inconsistency and can facilitate cross-lingual stance detection in general. another drawback of the previous method @xcite is that it only considers cross-language contrastive alignment and ignores the in-language target relation learning and contrastive alignments. therefore, in our work, we consider both target-level modeling and languagelevel alignments, and develop our computational method with in-language and cross-language solutions to tackle the target inconsistency issue for cross-lingual stance detection. ## proposed method figure 1 illustrates the overall structure of our proposed method tara. we first encode the input target and text with the encoder module and get the textual representations. then, we construct the target relation graph to learn both in-language and cross-language associations between targets and get target representations with aggregated information. after that, we concatenate the textual representations and target representations for classification. meanwhile, we align the representations with related targets within and across languages using the target relation alignment strategies. ## conclusion we first identify the issue of target inconsistency in cross-lingual stance detection and propose a target-oriented relation alignment method tara, which considers relation associations and language alignments at both target and language levels. our method explores the in-language and crosslanguage associations of targets via target relation graphs and aligns samples between highly correlated targets within the language and across languages through the fine-grained relation alignment strategies. experimental results demonstrate the effectiveness of our method for cross-lingual stance detection.