Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

NAACL 2016  ·  Ji Young Lee, Franck Dernoncourt ·

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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Dialogue Act Classification Switchboard corpus CNN[[Lee and Dernoncourt2016]] Accuracy 73.1 # 10

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