Challenge: Existing knowledge graphs focus on the representation and reasoning of general factual knowledge, while there are significant deficiencies in the understanding and reasoning for emotional knowledge.
Approach: They propose a commonsense knowledge graph that can be used to represent emotional knowledge by combining theories from psychology, cognitive science, and linguistics.
Outcome: The proposed model surpasses GPT-4-Turbo in the emotion-related tasks.

Similar Papers

From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues (2023.emnlp-main)

Copied to clipboard

Challenge: Understanding emotions during conversation is a fundamental aspect of human communication.
Approach: They propose an approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions.
Outcome: The proposed approach improves ERC for code-mixed conversations by integrating commonsense with dialogue context.
Multiple Knowledge-Enhanced Interactive Graph Network for Multimodal Conversational Emotion Recognition (2024.findings-emnlp)

Copied to clipboard

Challenge: Multimodal Emotion Recognition in Conversations models struggle due to lack of Common Sense Knowledge (CSK).
Approach: They propose a multimodal approach to integrate multiple knowledge into the edge representations by integrating textual and visual CSK.
Outcome: The proposed model outperforms state-of-the-art methods on two popular datasets.
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (D19-1)

Copied to clipboard

Challenge: Existing methods to analyze emotions in textual conversations are limited . emotion detection is challenging because humans rely on context and commonsense knowledge to express emotions .
Approach: They propose a Knowledge-Enriched Transformer where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged.
Outcome: The proposed model outperforms state-of-the-art models on most of the tested datasets in F1 score.
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (2020.findings-emnlp)

Copied to clipboard

Challenge: Current methods for emotion recognition in conversations often face difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes.
Approach: They propose a framework that incorporates mental states, events, and causal relations to learn interactions between interlocutors participating in a conversation.
Outcome: The proposed framework improves on four conversational benchmark datasets.
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)

Copied to clipboard

Challenge: Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description.
Approach: They propose a large-scale dataset to facilitate the study of KG-to-text . they propose MGCN model architecture that incorporates aggregation methods to extract the rich graph information.
Outcome: The proposed model can represent the original graph information more comprehensively and integrates multiple aggregation methods to extract the rich graph information.
PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives (2023.acl-long)

Copied to clipboard

Challenge: a new knowledge graph for personas based on human-validated persona facts is constructed to model diverse persona attributes . a variety of persona characteristics are required to sustain coherent narratives .
Approach: They construct a large-scale persona commonsense knowledge graph with 100K human-validated persona facts.
Outcome: The proposed graph contains rich and precise world persona inferences that help systems generate more consistent and engaging narratives.
An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations (2023.findings-emnlp)

Copied to clipboard

Challenge: Existing efforts in ERC focus on context- and speaker-sensitive dependencies, but lack of annotated data and high cost of obtaining such knowledge is a blank slate.
Approach: They propose a Multiple Knowledge Fusion Model to integrate multiple knowledge generated by Large Language Models (LLMs) they analyze the contribution and complementarity of this knowledge into the model.
Outcome: The proposed model integrates multiple knowledge generated by LLMs and analyzes its contribution and complementarity on three public datasets.
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)

Copied to clipboard

Challenge: In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge.
Approach: This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning.
Outcome: This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias).
Great~Truths~are ~Always ~Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models (2022.findings-naacl)

Copied to clipboard

Challenge: Existing approaches to enhance pre-trained language models (PTMs) with a knowledge-aware graph neural network (GNN) encoder that models a commonsense knowledge graph (CSKG) can't explain how external knowledge resources improve the reasoning capacity of PTMs.
Approach: They propose to use relation features from CSKGs to enhance the reasoning capacity of pre-trained language models (PTMs) by encoding a commonsense knowledge graph (CSKG)
Outcome: The proposed approach reduces the parameters for encoding CSKGs and improves on five benchmarks.
IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions (2023.emnlp-main)

Copied to clipboard

Challenge: Prior work on IE comprehension has focused on detecting idiomaticity, but this fails to account for IEs' non-compositionality.
Approach: They construct a commonsense knowledge graph for figurative interpretations of IEs that can be used to convert PTLMs into knowledge models that encode and infer commonsensical knowledge related to IE use.
Outcome: The proposed model can generalize to IEs unseen during training.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations