Stance detection aims to automatically determine whether the author is in favor of or against a given target.In principle,the sentiment information of a post highly influences the stance.In this study,we aim to levera...Stance detection aims to automatically determine whether the author is in favor of or against a given target.In principle,the sentiment information of a post highly influences the stance.In this study,we aim to leverage the sentiment information of a post to improve the performance of stance detection.However,conventional discrete models with sentimental features can cause error propagation.We thus propose a joint neural network model to predict the stance and sentiment of a post simultaneously,because the neural network model can learn both representation and interaction between the stance and sentiment collectively.Specifically, we first learn a deep shared representation between stance and sentiment information,and then use a neural stacking model to leverage sentimental information for the stance detection task.Empirical studies demonstrate the effectiveness of our proposed joint neural model.展开更多
Stance detection is the view towards a specific target by a given context(e.g.tweets,commercial reviews).Target-related knowledge is often needed to assist stance detection models in understanding the target well and ...Stance detection is the view towards a specific target by a given context(e.g.tweets,commercial reviews).Target-related knowledge is often needed to assist stance detection models in understanding the target well and making detection correctly.However,prevailing works for knowledge-infused stance detection predominantly incorporate target knowledge from a singular source that lacks knowledge verification in limited domain knowledge.The low-resource training data further increase the challenge for the data-driven large models in this task.To address those challenges,we propose a collaborative knowledge infusion approach for low-resource stance detection tasks,employing a combination of aligned knowledge enhancement and efficient parameter learning techniques.Specifically,our stance detection approach leverages target background knowledge collaboratively from different knowledge sources with the help of knowledge alignment.Additionally,we also introduce the parameter-efficient collaborative adaptor with a staged optimization algorithm,which collaboratively addresses the challenges associated with low-resource stance detection tasks from both network structure and learning perspectives.To assess the effectiveness of our method,we conduct extensive experiments on three public stance detection datasets,including low-resource and cross-target settings.The results demonstrate significant performance improvements compared to the existing stance detection approaches.展开更多
The task of cross-target stance detection faces significant challenges due to the lack of additional background information in emerging knowledge domains and the colloquial nature of language patterns.Traditional stan...The task of cross-target stance detection faces significant challenges due to the lack of additional background information in emerging knowledge domains and the colloquial nature of language patterns.Traditional stance detection methods often struggle with understanding limited context and have insufficient generalization across diverse sentiments and semantic structures.This paper focuses on effectively mining and utilizing sentimentsemantics knowledge for stance knowledge transfer and proposes a sentiment-aware hierarchical attention network(SentiHAN)for cross-target stance detection.SentiHAN introduces an improved hierarchical attention network designed to maximize the use of high-level representations of targets and texts at various fine-grain levels.This model integrates phrase-level combinatorial sentiment knowledge to effectively bridge the knowledge gap between known and unknown targets.By doing so,it enables a comprehensive understanding of stance representations for unknown targets across different sentiments and semantic structures.The model’s ability to leverage sentimentsemantics knowledge enhances its performance in detecting stances that may not be directly observable from the immediate context.Extensive experimental results indicate that SentiHAN significantly outperforms existing benchmark methods in terms of both accuracy and robustness.Moreover,the paper employs ablation studies and visualization techniques to explore the intricate relationship between sentiment and stance.These analyses further confirm the effectiveness of sentence-level combinatorial sentiment knowledge in improving stance detection capabilities.展开更多
基金the National Natural Science Foundation of China (Grant Nos.61331011,61751206,61773276,61672366)Jiangsu Provincial Science and Technology Plan (BK20151222)Project of Natural Science Research of the Universities of Jiangsu Province.
文摘Stance detection aims to automatically determine whether the author is in favor of or against a given target.In principle,the sentiment information of a post highly influences the stance.In this study,we aim to leverage the sentiment information of a post to improve the performance of stance detection.However,conventional discrete models with sentimental features can cause error propagation.We thus propose a joint neural network model to predict the stance and sentiment of a post simultaneously,because the neural network model can learn both representation and interaction between the stance and sentiment collectively.Specifically, we first learn a deep shared representation between stance and sentiment information,and then use a neural stacking model to leverage sentimental information for the stance detection task.Empirical studies demonstrate the effectiveness of our proposed joint neural model.
基金supported by the RCA founding of A*STAR and DSO National Laboratory(Nos.2208-526-RCA-CFAR and SC23/22-3204FA)。
文摘Stance detection is the view towards a specific target by a given context(e.g.tweets,commercial reviews).Target-related knowledge is often needed to assist stance detection models in understanding the target well and making detection correctly.However,prevailing works for knowledge-infused stance detection predominantly incorporate target knowledge from a singular source that lacks knowledge verification in limited domain knowledge.The low-resource training data further increase the challenge for the data-driven large models in this task.To address those challenges,we propose a collaborative knowledge infusion approach for low-resource stance detection tasks,employing a combination of aligned knowledge enhancement and efficient parameter learning techniques.Specifically,our stance detection approach leverages target background knowledge collaboratively from different knowledge sources with the help of knowledge alignment.Additionally,we also introduce the parameter-efficient collaborative adaptor with a staged optimization algorithm,which collaboratively addresses the challenges associated with low-resource stance detection tasks from both network structure and learning perspectives.To assess the effectiveness of our method,we conduct extensive experiments on three public stance detection datasets,including low-resource and cross-target settings.The results demonstrate significant performance improvements compared to the existing stance detection approaches.
基金supported by the National Social Science Fund of China(20BXW101)。
文摘The task of cross-target stance detection faces significant challenges due to the lack of additional background information in emerging knowledge domains and the colloquial nature of language patterns.Traditional stance detection methods often struggle with understanding limited context and have insufficient generalization across diverse sentiments and semantic structures.This paper focuses on effectively mining and utilizing sentimentsemantics knowledge for stance knowledge transfer and proposes a sentiment-aware hierarchical attention network(SentiHAN)for cross-target stance detection.SentiHAN introduces an improved hierarchical attention network designed to maximize the use of high-level representations of targets and texts at various fine-grain levels.This model integrates phrase-level combinatorial sentiment knowledge to effectively bridge the knowledge gap between known and unknown targets.By doing so,it enables a comprehensive understanding of stance representations for unknown targets across different sentiments and semantic structures.The model’s ability to leverage sentimentsemantics knowledge enhances its performance in detecting stances that may not be directly observable from the immediate context.Extensive experimental results indicate that SentiHAN significantly outperforms existing benchmark methods in terms of both accuracy and robustness.Moreover,the paper employs ablation studies and visualization techniques to explore the intricate relationship between sentiment and stance.These analyses further confirm the effectiveness of sentence-level combinatorial sentiment knowledge in improving stance detection capabilities.
