@InProceedings{saxena-bhat-pedanekar:2018:SocialNLP2018,
  author    = {Saxena, Rohit  and  bhat, savita  and  Pedanekar, Niranjan},
  title     = {EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling},
  booktitle = {Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-3509}
}