Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks
This addresses the challenge of leveraging sequential context in short-text classification for applications like dialog systems, though it is incremental as it builds on existing neural network methods.
The paper tackled the problem of classifying sequential short texts by incorporating preceding context, achieving state-of-the-art results on three dialog act prediction datasets.
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.