Graph Based Network with Contextualized Representations of Turns in Dialogue
Bongseok Lee, Yong Suk Choi

TL;DR
This paper introduces TUCORE-GCN, a graph convolutional network that models dialogue context for relation extraction and emotion recognition, outperforming existing models on multiple dialogue understanding benchmarks.
Contribution
The paper presents TUCORE-GCN, a novel graph-based model that effectively captures dialogue context for relation extraction and emotion recognition tasks.
Findings
Outperforms state-of-the-art models on dialogue-based RE datasets
Effective in emotion recognition in conversations
Demonstrates strong generalization across multiple dialogue understanding tasks
Abstract
Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogue-based relation extraction requires a comprehensive understanding of dialogue. In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues. In addition, we propose a novel approach which treats the task of emotion recognition in conversations (ERC) as a dialogue-based RE. Experiments on a dialogue-based RE dataset and three ERC datasets demonstrate that our model is very effective in various dialogue-based natural language understanding tasks. In these experiments, TUCORE-GCN…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Sentiment Analysis and Opinion Mining · Natural Language Processing Techniques
MethodsAttentive Walk-Aggregating Graph Neural Network
