The Open UniversitySkip to content
 

Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis.

Zhang, Yazhou; Li, Qiuchi; Song, Dawei; Zhang, Peng and Wang, Panpan (2019). Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis. In: 28th International Joint Conference on Artificial Intelligence (IJCAI2019), 10-16 Aug 2019, Macau, China.

Full text available as:
[img]
Preview
PDF (Version of Record) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview
URL: https://www.ijcai19.org
Google Scholar: Look up in Google Scholar

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.

Item Type: Conference or Workshop Item
Copyright Holders: 2019 The Authors
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 61755
Depositing User: Dawei Song
Date Deposited: 17 Jun 2019 09:39
Last Modified: 06 Sep 2019 22:12
URI: http://oro.open.ac.uk/id/eprint/61755
Share this page:

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU