DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
Abstract
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.
Cite
Text
Majumder et al. "DialogueRNN: An Attentive RNN for Emotion Detection in Conversations." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33016818Markdown
[Majumder et al. "DialogueRNN: An Attentive RNN for Emotion Detection in Conversations." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/majumder2019aaai-dialoguernn/) doi:10.1609/AAAI.V33I01.33016818BibTeX
@inproceedings{majumder2019aaai-dialoguernn,
title = {{DialogueRNN: An Attentive RNN for Emotion Detection in Conversations}},
author = {Majumder, Navonil and Poria, Soujanya and Hazarika, Devamanyu and Mihalcea, Rada and Gelbukh, Alexander F. and Cambria, Erik},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {6818-6825},
doi = {10.1609/AAAI.V33I01.33016818},
url = {https://mlanthology.org/aaai/2019/majumder2019aaai-dialoguernn/}
}