AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification
This work addresses emotion classification for natural language processing applications, but it is incremental as it builds on existing attention and CNN methods.
The authors tackled multi-label emotion classification by proposing an attention-based convolutional neural network that imitates human sentence understanding, achieving 5th rank in English and 1st rank in Spanish at SemEval-2018 Task 1.
In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human's two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given sentiments. Our model achieves 5-th/1-th rank in English/Spanish respectively.