ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN
This work addresses emotion classification for conversational AI, but it is incremental as it builds on existing RCNN methods with pre-trained representations.
The paper tackled emotion detection in textual conversations by extending a Recurrent Convolutional Neural Network with fine-tuned word and DeepMoji sentence representations, achieving a micro-F1 score of 0.7463 without handcrafted features.
In this paper we present our model on the task of emotion detection in textual conversations in SemEval-2019. Our model extends the Recurrent Convolutional Neural Network (RCNN) by using external fine-tuned word representations and DeepMoji sentence representations. We also explored several other competitive pre-trained word and sentence representations including ELMo, BERT and InferSent but found inferior performance. In addition, we conducted extensive sensitivity analysis, which empirically shows that our model is relatively robust to hyper-parameters. Our model requires no handcrafted features or emotion lexicons but achieved good performance with a micro-F1 score of 0.7463.