A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness (D18-1)
Copied to clipboard
| Challenge: | Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. |
| Approach: | They propose a co-attention neural network model for emotion cause analysis with emotional context awareness. |
| Outcome: | The proposed model outperforms the state-of-the-art methods. |
Similar Papers
Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. |
| Approach: | They propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. |
| Outcome: | The proposed method outperforms existing methods in the extraction of emotion-cause pairs . it emphasizes inter-clause modeling to perform end-to-end extraction . |
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis (D19-1)
Copied to clipboard
| Challenge: | Emotion cause analysis aims to identify the reasons behind emotions . previous models focus on learning architecture with local textual information . |
| Approach: | They propose a method to extract emotion cause with hierarchical neural model and knowledge-based regularizations by sentiment lexicon and common knowledge. |
| Outcome: | The proposed method outperforms baselines on two public datasets in different languages and outperformed competitive baselines by 2.08%. |
Joint Learning for Emotion Classification and Emotion Cause Detection (D18-1)
Copied to clipboard
| Challenge: | Using a unified framework, we propose a joint approach for emotion classification and emotion cause detection. |
| Approach: | They propose a neural network-based joint approach for emotion classification and emotion cause detection which captures mutual benefits across the two sub-tasks. |
| Outcome: | The proposed approach can capture mutual benefits across two sub-tasks on Chinese microblogs. |
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis (D19-1)
Copied to clipboard
| Challenge: | Multi-modal analysis is a field emerging in the fields of natural language processing, computer vision and speech processing . multimodal analysis uses a variety of information from multiple sources to build efficient systems . acoustic and visual information can provide better information for classification decisions . |
| Approach: | They propose a recurrent neural network based approach for multi-modal sentiment and emotion analysis . they employ a context-aware attention module to exploit the correspondence among neighboring utterances . |
| Outcome: | The proposed model learns inter-modal interaction among participating modalities through auto-encoder mechanism . it is compared with existing state-of-the-art models on five standard multi-modal affect analysis datasets . |
Bidirectional Hierarchical Attention Networks based on Document-level Context for Emotion Cause Extraction (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Emotion cause extraction (ECE) aims to extract the causes behind certain emotion in text. |
| Approach: | They propose a bidirectional hierarchical attention network corresponding to the specified candidate cause clause to capture document-level context in a structured and dynamic manner. |
| Outcome: | The proposed method achieves competitive performances on two public datasets in Chinese and English. |
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction (2021.findings-acl)
Copied to clipboard
Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
| Challenge: | Detecting what emotions are expressed in text is a well-studied problem in natural language processing. |
| Approach: | They propose methods that combine common-sense knowledge with multi-task learning to perform joint emotion classification and emotion cause tagging. |
| Outcome: | The proposed models improve on both tasks when using common-sense reasoning and a multitask framework. |
ECERC: Evidence-Cause Attention Network for Multi-Modal Emotion Recognition in Conversation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for multi-modal emotion recognition in isolated utterances do not capture emotional causes, including emotional contagion, influences from others, and self-referenced or externally introduced events. |
| Approach: | They propose a multi-modal conversational emotion recognition system that integrates emotional evidence with contextual causes through five stages. |
| Outcome: | The proposed method achieves competitive performance on two widely used benchmark datasets, IEMOCAP and MELD. |
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts (P19-1)
Copied to clipboard
| Challenge: | Emotion cause extraction (ECE) aims at extracting potential causes behind certain emotions in text. |
| Approach: | They propose a 2-step task to extract potential pairs of emotions and corresponding causes in a document. |
| Outcome: | The proposed task is based on a benchmark emotion cause corpus. |
Enhancing Emotion-Cause Pair Extraction in Conversations via Center Event Detection and Reasoning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify emotion utterances and their corresponding cause utterrances in unannotated conversations. |
| Approach: | They propose a new method to identify emotion utterances and their corresponding cause utterrances in unannotated conversations by using a center event-aware graph. |
| Outcome: | The proposed model outperforms existing methods and achieves state-of-the-art performance across three benchmark datasets. |
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies on ERC lack the ability to extract and integrate emotional clues from the conversational context. |
| Approach: | They propose a new model that uses multi-turn reasoning modules to extract and integrate emotional clues from conversational context. |
| Outcome: | The proposed model outperforms existing models on three public benchmark datasets and is highly effective and superior to existing models. |