Nowadays, talking to conversational agents is becoming a daily routine, and it is crucial for dialogue systems to generate responses as human-like as possible. As one of the main aspects, primary attention should be given to providing emotionally aware responses to users. In this article, we are going to describe the recurrent neural network architecture for emotion detection in textual conversations, that participated in SemEval-2019 Task 3 “EmoContext”, that is, an annual workshop on semantic evaluation. The task objective is to classify emotion (i.e. happy, sad, angry, and others) in a 3-turn conversational data set.

The rest of the article is organized as follows. Section 1 gives a brief overview of the EmoContext task and the provided data. Sections 2 and 3 focus on the texts pre-processing and word embeddings, consequently. In section 4, we described the architecture of the LSTM model used in our submission. In conclusion, the final performance of our system and the source code are presented. The model is implemented in Python using Keras library.

https://habr.com/en/company/mailru/blog/439850/