With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
Object detection serves as a challenging yet crucial task in computer vision.Despite significant advancements,modern detectors remain struggling with task alignment between localization and classification.In this pape...Object detection serves as a challenging yet crucial task in computer vision.Despite significant advancements,modern detectors remain struggling with task alignment between localization and classification.In this paper,Global Collaborative Learning(GCL)is introduced to address these challenges from often-overlooked perspectives.First,the essence of GCL is reflected in the label assignment of the detector.Adjusting the loss function to transform samples with strong localization yet weak classification into high-quality samples in both tasks,provides more effective training signals,enabling the model to capture key consistent features.Second,the spirit of GCL is embodied in the head design.By enabling global feature interaction within the decoupled head,the approach ensures that final predictions are made more comprehensively and robustly,thereby preventing the two independent branches from converging into suboptimal solutions for their respective tasks.Extensive experiments on the challenging MS COCO and CrowdHuman datasets demonstrate that the proposed GCL method substantially enhances performance and generalization capabilities.展开更多
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金supported by National Key R&D Program of China under Grant No.2022YFB3305700Shanghai Science Innovation Action Plan under Grant No.21511104302.
文摘Object detection serves as a challenging yet crucial task in computer vision.Despite significant advancements,modern detectors remain struggling with task alignment between localization and classification.In this paper,Global Collaborative Learning(GCL)is introduced to address these challenges from often-overlooked perspectives.First,the essence of GCL is reflected in the label assignment of the detector.Adjusting the loss function to transform samples with strong localization yet weak classification into high-quality samples in both tasks,provides more effective training signals,enabling the model to capture key consistent features.Second,the spirit of GCL is embodied in the head design.By enabling global feature interaction within the decoupled head,the approach ensures that final predictions are made more comprehensively and robustly,thereby preventing the two independent branches from converging into suboptimal solutions for their respective tasks.Extensive experiments on the challenging MS COCO and CrowdHuman datasets demonstrate that the proposed GCL method substantially enhances performance and generalization capabilities.
