ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT
This work addresses emotion classification in dialogues, which is an incremental improvement for natural language processing applications.
The paper tackled emotion detection in conversational contexts by proposing a hierarchical LSTM model (HRLCE) that outperformed BERT, achieving a score of 0.7709 and ranking 5th among 165 teams in SemEval-2019 Task 3.
This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchical LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational context. The results show that, in this task, our HRCLE outperforms the most recent state-of-the-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.