2021/09/08 by Bongseok Lee, Yong Suk Choi, Lee, Bongseok +1 · 2 citations
Computer Science · #Artificial intelligence #Benchmark (surveying) #Code (set theory) #Computation and Language (cs.CL) #Computer science #Context (archaeology) #Data mining #FOS: Computer and information sciences #Graph #Information extraction #Linguistics #Natural Language Processing Techniques #Natural language processing #Pronoun #Relation (database) #Relationship extraction #Sentence #Sentiment Analysis and Opinion Mining #Task (project management) #Theoretical computer science #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2109.04008
published in arXiv (Cornell University), 443-455 (Cornell University) · EMNLP 2021
arxiv created 2021/09/09 · openalex publication_date 2021/09/09 · arxiv updated 2021/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/09
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 outperforms the state-of-the-art models on most of the benchmark datasets. Our code is available at https://github.com/BlackNoodle/TUCORE-GCN.