@inproceedings{delbrouck-etal-2020-transformer,
    title = "A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis",
    author = "Delbrouck, Jean-Benoit  and
      Tits, No{\'e}  and
      Brousmiche, Mathilde  and
      Dupont, St{\'e}phane",
    editor = "Zadeh, Amir  and
      Morency, Louis-Philippe  and
      Liang, Paul Pu  and
      Poria, Soujanya",
    booktitle = "Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.challengehml-1.1",
    doi = "10.18653/v1/2020.challengehml-1.1",
    pages = "1--7",
    abstract = "Understanding expressed sentiment and emotions are two crucial factors in human multimodal language. This paper describes a Transformer-based joint-encoding (TBJE) for the task of Emotion Recognition and Sentiment Analysis. In addition to use the Transformer architecture, our approach relies on a modular co-attention and a glimpse layer to jointly encode one or more modalities. The proposed solution has also been submitted to the ACL20: Second Grand-Challenge on Multimodal Language to be evaluated on the CMU-MOSEI dataset. The code to replicate the presented experiments is open-source .",
}