@inproceedings{saxena-etal-2018-emotionx,
    title = "{E}motion{X}-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling",
    author = "Saxena, Rohit  and
      Bhat, Savita  and
      Pedanekar, Niranjan",
    editor = "Ku, Lun-Wei  and
      Li, Cheng-Te",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W18-3509/",
    doi = "10.18653/v1/W18-3509",
    pages = "50--55",
    abstract = "This paper presents our system submitted to the EmotionX challenge. It is an emotion detection task on dialogues in the EmotionLines dataset. We formulate this as a hierarchical network where network learns data representation at both utterance level and dialogue level. Our model is inspired by Hierarchical Attention network (HAN) and uses pre-trained word embeddings as features. We formulate emotion detection in dialogues as a sequence labeling problem to capture the dependencies among labels. We report the performance accuracy for four emotions (anger, joy, neutral and sadness). The model achieved unweighted accuracy of 55.38{\%} on Friends test dataset and 56.73{\%} on EmotionPush test dataset. We report an improvement of 22.51{\%} in Friends dataset and 36.04{\%} in EmotionPush dataset over baseline results."
}