Sequential Short Text Classification with Recurrent and Convolutional Neural Networks
Recent approaches based on artificial neural networks (ANNs) have shown promising results for short text classification. However, many short texts occur in sequences (e.g. sentences in a document or utterances in a dialog) and most existing ANN based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state of the art results on three different datasets for dialog act prediction.
NAACL