Deep Broad Learning for Emotion Classification in Textual Conversations  

作　　者：Sancheng Peng Rong Zeng Hongzhan Liu Lihong Cao Guojun Wang Jianguo Xie 

机构地区：[1]Center for Linguistics and Applied Linguistics,and Laboratory of Language Engineering and Computing,Guangdong University of Foreign Studies,Guangzhou 510006,China [2]Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices,South China Normal University,Guangzhou 510006,China [3]School of Computer Science and Cyber Engineering,Guangzhou University,Guangzhou 510006,China [4]Modern Education Technology Center,Guangdong University of Foreign Studies,Guangzhou 510006,China

出　　处：《Tsinghua Science and Technology》2024年第2期481-491,共11页清华大学学报（自然科学版（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61876205);the National Key Research and Development Program of China(No.2020YFB1005804);the MOE Project at Center for Linguistics and Applied Linguistics,Guangdong University of Foreign Studies.

摘　　要：Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual conversations.It is becoming one of the most important tasks for natural language processing in recent years.However,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual context.To address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely DBL.It aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad learning.Extensive experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion classification.In addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.

关 键 词：emotion classification textual conversation Convolutional Neural Network(CNN) Bidirectional Long Short-Term Memory(Bi-LSTM) broad learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] J218.2[艺术—美术]