Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis.
Yazhou Zhang,Qiuchi Li,Dawei Song,Dawei Song,Peng Zhang,Panpan Wang +5 more
- pp 5436-5442
Reads0
Chats0
TLDR
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.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.read more
Citations
More filters
Journal ArticleDOI
Multimodal Emotion Recognition With Transformer-Based Self Supervised Feature Fusion
TL;DR: This work introduces a novel Transformers and Attention-based fusion mechanism that can combine multimodal SSL features and achieve state-of-the-art results for the task of multi-modal emotion recognition.
Journal ArticleDOI
A Quantum-Like multimodal network framework for modeling interaction dynamics in multiparty conversational sentiment analysis
Yazhou Zhang,Dawei Song,Dawei Song,Xiang Li,Peng Zhang,Panpan Wang,Lu Rong,Guangliang Yu,Bo Wang +8 more
TL;DR: A novel and comprehensive framework for multimodal sentiment analysis in conversations is proposed, called a quantum-like multi-modal network (QMN), which leverages the mathematical formalism of quantum theory (QT) and a long short-term memory (LSTM) network.
Journal ArticleDOI
Conversational transfer learning for emotion recognition
TL;DR: This paper proposed TL-ERC, where they pre-train a hierarchical dialogue model on multi-turn conversations and then transfer its parameters to a conversational emotion classifier (target) using pre-trained sentence encoders and recurrent parameters that model inter-sentential context across the whole conversation.
Journal ArticleDOI
CFN: A Complex-valued Fuzzy Network for Sarcasm Detection in Conversations
Yazhou Zhang,Yaochen Liu,Qiuchi Li,Prayag Tiwari,Benyou Wang,Yuhua Li,Hari Mohan Pandey,Peng Zhang,Dawei Song +8 more
TL;DR: A complex-valued fuzzy network is proposed by leveraging the mathematical formalisms of quantum theory and fuzzy logic to address the intrinsic vagueness and uncertainty of human language in emotional expression and understanding in sarcasm detection.
Journal ArticleDOI
Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis.
TL;DR: A new conversational dataset is presented, named ScenarioSA, and an interactive long short-term memory network is proposed for conversational sentiment analysis to model interactions between speakers in a conversation, which outperforms a wide range of strong baselines and achieves competitive results with the state-of-art approaches.
References
More filters
Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Proceedings ArticleDOI
Convolutional Neural Networks for Sentence Classification
TL;DR: The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.
Posted Content
Convolutional Neural Networks for Sentence Classification
TL;DR: In this article, CNNs are trained on top of pre-trained word vectors for sentence-level classification tasks and a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks.
Proceedings ArticleDOI
Attention-based LSTM for Aspect-level Sentiment Classification
TL;DR: This paper reveals that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect, and proposes an Attention-based Long Short-Term Memory Network for aspect-level sentiment classification.
Book ChapterDOI
Measures on the Closed Subspaces of a Hilbert Space
TL;DR: In this paper, a measure on the closed subspaces of a Hilbert space is defined, which assigns to every closed subspace a non-negative real number such that if the subspace is a countable collection of mutually orthogonal sub-spaces having closed linear span B, then