DHF-Net : a hierarchical feature interactive fusion network for dialogue emotion recognition

Gan, Chenquan (author), Yang, Yucheng (author), Zhu, Qingyi (author), Jain, Deepak Kumar (author), Štruc, Vitomir (author)

To balance the trade-off between contextual information and fine-grained information in identifying specific emotions during a dialogue and combine the interaction of hierarchical feature related information, this paper proposes a hierarchical feature interactive fusion network (named DHF-Net), which not only can retain the integrity of the context sequence information but also can extract more fine-grained information. To obtain a deep semantic information, DHF-Net processes the task of recognizing dialogue emotion and dialogue act/intent separately, and then learns the cross-impact of two tasks through collaborative attention. Also, a bidirectional gate recurrent unit (Bi-GRU) connected hybrid convolutional neural network (CNN) group method is designed, by which the sequence information is smoothly sent to the multi-level local information layers for feature exaction. Experimental results show that, on two open session datasets, the performance of DHF-Net is improved by 1.8% and 1.2%, respectively.

• Language: English
• Keywords: emotion recognition, text processing, NLP, deep learning, dialogue emotion recognition, contextual information, fine-grained information, hierarchical feature, interactive fusion
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2022
• Number of pages: 8 str.
• Numbering: Vol. 210, art. 118525
• PID: 20.500.12556/RUL-139610
• UDC: 004.9:004.8
• ISSN on article: 0957-4174
• DOI: 10.1016/j.eswa.2022.118525
• COBISS.SI-ID: 119131395
• Publication date in RUL: 06.09.2022

Record is a part of a journal

• Title: Expert systems with applications
• Shortened title: Expert syst. appl.
• Publisher: Elsevier
• ISSN: 0957-4174
• COBISS.SI-ID: 171291

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: razpoznavanje emocij, procesiranje besedil, dialog, NLP, globoko učenje