超图神经网络用于对话中的情感识别

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2023-12-27 DOI:10.1145/3638760

Cheng Zheng, Haojie Xu, Xiao Sun

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引用次数: 0

摘要

会话语境建模是会话中情感识别的关键步骤。现有研究仍存在对本地语境信息和远程语境信息利用不足的问题。本文设计了一种超图神经网络，即 HNN-ERC，以更好地利用本地和远程语境信息。HNN-ERC 将递归神经网络与传统的超图神经网络相结合，加强了语篇之间的联系，使每个语篇都能更好地接收来自其他语篇的信息。所提出的模型在三个基准数据集上取得了最先进的实证结果，证明了新模型的有效性和优越性。

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Hypergraph Neural Network for Emotion Recognition in Conversations

Modeling conversational context is an essential step for emotion recognition in conversations. Existing works still suffer from insufficient utilization of local context information and remote context information. This paper designs a hypergraph neural network, namely HNN-ERC, to better utilize local and remote contextual information. HNN-ERC combines the recurrent neural network with the conventional hypergraph neural network to strengthen connections between utterances and make each utterance receive information from other utterances better. The proposed model has empirically achieved state-of-the-art results on three benchmark datasets, demonstrating the effectiveness and superiority of the new model.

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来源期刊

ACM Transactions on Asian and Low-Resource Language Information Processing Computer Science-General Computer Science

CiteScore

3.60

自引率

15.00%

发文量

241

期刊介绍： The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.