DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

By Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, Erik Cambria. AAAI Conference on Artificial Intelligence, 2018

973 citations recorded by the source corpus.

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, and so on. Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state-of-the-art by a significant margin on two different datasets.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/3a7895b17db0cda7bbf86bcda52c46a3e03b6ded

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Citations in the corpus

Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020, background): “Majumder et al. (2019) proposes the DialogueRNN model that exploits three gated recurrent units (GRU) (Cho et al., 2014) to capture speaker information, context and emotional information of the preceding utterances, respectively.”
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances (2019, background): “DialogueRNN [11] aims to solve this issue by considering the speaker information of the target utterance and, further, modeling self and inter-speaker emotional influence with a hierarchical multi-stage RNN with attention mechanism.”
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (2019, methodology): “DialogueRNN (Majumder et al., 2019): The stateof-the-art model for emotion detection in textual conversations.”
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (2020, methodology): “Compared to the recently published works on ERC (Chen et al., 2018; Ma-jumder et al., 2019; Zhou et al., 2018; Qin et al., 2020; Zhong et al., 2019; Zhang et al., 2019), both lexicon-based (Wu et al., 2006; Mohammad and Turney, 2010; Shaheen et al., 2014) and modern deep learning-based (Kratzwald et”
Challenges in Building Intelligent Open-domain Dialog Systems (2019, background): “Another issue of most existing work is that the user’s emotion transition during a conversation [82] is not explicitly modeled.”
A survey on empathetic dialogue systems (2020, background): “Existing work has shown that multimodality helps to improve the accuracy of emotion detection from dialogue [143, 144, 145, 146, 147].”
MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation (2021, methodology): “, 2018a), and DialogueRNN model (Majumder et al., 2019) etc.”
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (2019, methodology): “DialogueRNN (Majumder et al., 2019) This is the state-of-the-art method for ERC.”
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (2018, background): “DialogueRNN represents the current state of the art for conversational emotion detection (Majumder et al., 2019).”
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, neutral): “[133], to grasp information from the context.”
Directed Acyclic Graph Network for Conversational Emotion Recognition (2021, methodology): “5% achieved by DialogueRNN (Majumder et al., 2019) and 55% achieved by DialogXL (Shen et al.”