Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos

@article{Hazarika2018ConversationalMN,
  title={Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos},
  author={Devamanyu Hazarika and Soujanya Poria and Amir Zadeh and E. Cambria and Louis-Philippe Morency and Roger Zimmermann},
  journal={Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting},
  year={2018},
  volume={2018},
  pages={
          2122-2132
        }
}
Emotion recognition in conversations is crucial for the development of empathetic machines. [...] Key Method The framework takes a multimodal approach comprising audio, visual and textual features with gated recurrent units to model past utterances of each speaker into memories. Such memories are then merged using attention-based hops to capture inter-speaker dependencies. Experiments show an accuracy improvement of 3−4% over the state of the art.Expand
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