Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

Understand

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.