SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in Spoken Dialogues and Chats
This work addresses emotion prediction in dialogues for NLP researchers, but it is incremental as it builds on existing datasets and methods.
The paper tackled the problem of predicting emotions in spoken and chat-based dialogues by organizing the EmotionX 2019 Challenge, which used augmented EmotionLines datasets, resulting in top micro-F1 scores of 81.5% for spoken dialogues and 79.5% for chat dialogues.
We present an overview of the EmotionX 2019 Challenge, held at the 7th International Workshop on Natural Language Processing for Social Media (SocialNLP), in conjunction with IJCAI 2019. The challenge entailed predicting emotions in spoken and chat-based dialogues using augmented EmotionLines datasets. EmotionLines contains two distinct datasets: the first includes excerpts from a US-based TV sitcom episode scripts (Friends) and the second contains online chats (EmotionPush). A total of thirty-six teams registered to participate in the challenge. Eleven of the teams successfully submitted their predictions performance evaluation. The top-scoring team achieved a micro-F1 score of 81.5% for the spoken-based dialogues (Friends) and 79.5% for the chat-based dialogues (EmotionPush).