{"type":"ScholarlyArticle","title":"RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction","authors":[{"name":"Rui Xia","slug":"rui-xia","given":"Rui","family":"Xia","affiliation":"School of Computer Science and Engineering , Nanjing University of Science and Technology , China"},{"name":"Mengran Zhang","slug":"mengran-zhang","given":"Mengran","family":"Zhang","affiliation":"School of Computer Science and Engineering , Nanjing University of Science and Technology , China"},{"name":"Zixiang Ding","slug":"zixiang-ding","given":"Zixiang","family":"Ding","affiliation":"School of Computer Science and Engineering , Nanjing University of Science and Technology , China"}],"abstract":"The emotion cause extraction (ECE) task aims at discovering the potential causes behind a certain emotion expression in a document. Techniques including rule-based methods, traditional machine learning methods and deep neural networks have been proposed to solve this task. However, most of the previous work considered ECE as a set of independent clause classification problems and ignored the relations between multiple clauses in a document. In this work, we propose a joint emotion cause extraction framework, named RNN-Transformer Hierarchical Network (RTHN), to encode and classify multiple clauses synchronously. RTHN is composed of a lower word-level encoder based on RNNs to encode multiple words in each clause, and an upper clause-level encoder based on Transformer to learn the correlation between multiple clauses in a document. We furthermore propose ways to encode the relative position and global predication information into Transformer that can capture the causality between clauses and make RTHN more efficient. We finally achieve the best performance among 12 compared systems and improve the F1 score of the state-of-the-art from 72.69% to 76.77%.","doi":"10.24963/ijcai.2019/734","url":"https://ijcai.org/proceedings/2019/734","pdf":"https://ijcai.org/proceedings/2019/0734.pdf","markdown":"https://ijcai.org/proceedings/2019/734.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/734","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":734,"pages":{"first":5285,"last":5291},"keywords":["Natural Language Processing: Sentiment Analysis and Text Mining","Natural Language Processing: Text Classification"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2010] Ying Chen, Sophia Yat Mei Lee, Shoushan Li, and Chu-Ren Huang. 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