Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks (1603.03827v1)

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.

• The paper demonstrates that integrating preceding text context with a hybrid CNN-RNN model significantly improves dialog act classification.
• The methodology transforms short texts into vector representations using LSTM and convolutional filters to capture sequential dependencies.
• Experimental results on datasets like SwDA show that the sequential approach outperforms traditional models and can be adapted for various NLP tasks.

Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks: An Overview

The paper "Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks" by Ji Young Lee and Franck Dernoncourt explores an advanced methodology for classifying sequences of short texts, such as sentences in a document or dialogues in a conversation, primarily focusing on dialog act prediction. The work is noteworthy for its integration of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) to leverage the context provided by preceding texts, a strategy seldom employed in previous Artificial Neural Network (ANN)-based approaches.

Methodology

The authors introduce a hybrid model that combines RNNs, specifically Long Short-Term Memory networks (LSTMs), with CNNs to create a robust short-text classification system that incorporates preceding texts. The paper outlines the two-part structure of the model:

1. Short-Text Representation: This process involves transforming a sequence of short texts into a vector representation using either an RNN or a CNN. The networks process $m$-dimensional word vectors to produce an $n$-dimensional vector through various pooling techniques, such as last, mean, and max pooling in RNNs, or using convolution operations with different filters in CNNs.
2. Sequential Classification: Building on these representations, the model predicts classes for the current text by considering vector representations of both the current and previous short texts. The classification is executed via a two-layer feedforward ANN that outputs a probability distribution over defined classes.

Experimental Results

The researchers tested the model on three datasets: DSTC 4, MRDA, and SwDA. The effectiveness of incorporating sequential information in the classification task was paramount. Both CNNs and LSTMs exhibited notable enhancements in prediction accuracy when contextual information was considered. Specifically, the CNN-based architecture demonstrated superior performance, particularly in the SwDA dataset, achieving a notable improvement compared to baseline models like Support Vector Machines (SVMs), Hidden Markov Models (HMMs), and Naive Bayes methods.

The sequential approach, quantified through hyperparameters such as the history sizes $d_1$ and $d_2$, revealed that utilizing preceding text representations significantly improved classification accuracy over using isolated short texts. This enhancement underscores the model's ability to capture dependencies in sequential data—an advantageous feature for tasks necessitating context awareness, such as dialog act classification.

Implications and Future Directions

The implications of leveraging ANN-based methodologies with sequential context insight extend beyond dialog act classification. The conceptual framework can be adapted to various NLP tasks requiring understanding of textual sequences, including sentiment analysis, question answering, and document classification. The reported advancements also suggest a potential reduction in dependency on feature engineering, given the model's ability to autonomously learn contextual representations.

Future avenues for research could involve exploring memory-efficient or low-latency models that sustain sequence-informed accuracy, given the computational demands of RNNs and CNNs. Additionally, further refinements might include utilizing transformer architectures, which have demonstrated scaling benefits in related NLP domains. Integrating such models with the sequential framework of this research may yield further advancements in capturing abstract dependencies over lengthy text sequences.

In conclusion, this exploration into sequential short-text classification using RNNs and CNNs marks a significant stride in ANN-based NLP systems, reflecting a crucial understanding of how prior textual sequences can enhance the comprehension and classification of current texts in context-rich applications.