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.- Anthology ID:
- W18-3509
- Volume:
- Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- SocialNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 50–55
- Language:
- URL:
- https://aclanthology.org/W18-3509
- DOI:
- 10.18653/v1/W18-3509
- Cite (ACL):
- Rohit Saxena, Savita Bhat, and Niranjan Pedanekar. 2018. EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling. In Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media, pages 50–55, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling (Saxena et al., SocialNLP 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W18-3509.pdf