{"type":"ScholarlyArticle","title":"Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis","authors":[{"name":"Yazhou Zhang","slug":"yazhou-zhang","given":"Yazhou","family":"Zhang","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China; Zhejiang Lab , Hangzhou , China"},{"name":"Qiuchi Li","slug":"qiuchi-li","given":"Qiuchi","family":"Li","affiliation":"Department of Information Engineering , University of Padua , Padua , Italy"},{"name":"Dawei Song","slug":"dawei-song","given":"Dawei","family":"Song","affiliation":"School of Computer Science and Technology , Beijing Institute of Technology , Beijing , China; School of Computing and Communications , The Open University , United Kingdom"},{"name":"Peng Zhang","slug":"peng-zhang","given":"Peng","family":"Zhang","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Panpan Wang","slug":"panpan-wang","given":"Panpan","family":"Wang","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"}],"abstract":"Conversational sentiment analysis is an emerging, yet challenging Artificial Intelligence (AI) subtask. It aims to discover the affective state of each participant in a conversation. There exists a wealth of interaction information that affects the sentiment of speakers. However, the existing sentiment analysis approaches are insufficient in dealing with this task due to ignoring the interactions and dependency relationships between utterances. In this paper, we aim to address this issue by modeling intrautterance and inter-utterance interaction dynamics. We propose an approach called quantum-inspired interactive networks (QIN), which leverages the mathematical formalism of quantum theory (QT) and the long short term memory (LSTM) network, to learn such interaction dynamics. Specifically, a density matrix based convolutional neural network (DM-CNN) is proposed to capture the interactions within each utterance (i.e., the correlations between words), and a strong-weak influence model inspired by quantum measurement theory is developed to learn the interactions between adjacent utterances (i.e., how one speaker influences another). Extensive experiments are conducted on the MELD and IEMOCAP datasets. The experimental results demonstrate the effectiveness of the QIN model.","doi":"10.24963/ijcai.2019/755","url":"https://ijcai.org/proceedings/2019/755","pdf":"https://ijcai.org/proceedings/2019/0755.pdf","markdown":"https://ijcai.org/proceedings/2019/755.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/755","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":755,"pages":{"first":5436,"last":5442},"keywords":["Agent-based and Multi-agent Systems: Human-Agent Interaction","Natural Language Processing: Sentiment Analysis and Text Mining","Natural Language Processing: Dialogue","Natural Language Processing: Text Classification"],"videos":[],"references":[{"position":1,"raw":"[Bhaskar et al., 2015] Jasmine Bhaskar, K Sruthi, and Prema Nedungadi. 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