EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling
Rohit Saxena, Savita Bhat, Niranjan Pedanekar · 2018
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.