Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

Ji Young Lee, Franck Dernoncourt

2016-03-12NAACL 2016 6Text Classification text-classification General Classification Classification

Abstract

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.

Results

Task	Dataset	Metric	Value	Model
Dialogue	Switchboard corpus	Accuracy	73.1	CNN[[Lee and Dernoncourt2016]]

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