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Compared with standard text, understanding dialogue is more challenging for machines as the dynamic and unexpected semantic changes in each turn.
The icsi meeting recorder dialog act (mrda) corpus
Elizabeth Shriberg, Raj Dhillon, Sonali Bhagat, Jeremy Ang, and Hannah Carvey · 2004
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Sequential short-text classification with recurrent and convolutional neural networks
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Dailydialog: A manually labelled multi-turn dialogue dataset
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Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks
Sayyed Zahiri and Jinho D. Choi · 2018
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Dialoguernn: An attentive rnn for emotion detection in conversations
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Graph based network with contextualized representations of turns in dialogue
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Deep context modeling for multi-turn response selection in dialogue systems
Lu Li, Chenliang Li, and Donghong Ji · 2021
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Filling the gap of utterance-aware and speaker-aware representation for multi-turn dialogue
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Dialogxl: All-in-one xlnet for multi-party conversation emotion recognition
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Gdpnet: Refining latent multi-view graph for relation extraction
Fuzhao Xue, Aixin Sun, Hao Zhang, and Eng Siong Chng · 2021
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Cosmic: Commonsense knowledge for emotion identification in conversations
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Speaker-aware bert for multi-turn response selection in retrieval-based chatbots
Jia-Chen Gu, Tianda Li, Quan Liu, Zhen-Hua Ling, Zhiming Su, Si Wei, and Xiaodan Zhu · 2020
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An embarrassingly simple model for dialogue relation extraction
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