Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection

TL;DR

This paper applies neural attention models to sequence classification tasks like dialogue act detection and key term extraction, demonstrating improved performance and analyzing the attention mechanism's role.

Contribution

It introduces the application of neural attention models to sequence labeling tasks and provides analysis and visualization of the attention mechanism's impact.

Findings

Attention improves sequence classification accuracy.

Attention highlights relevant parts of sequences.

Visual analysis of attention roles enhances understanding.

Abstract

Recurrent neural network architectures combining with attention mechanism, or neural attention model, have shown promising performance recently for the tasks including speech recognition, image caption generation, visual question answering and machine translation. In this paper, neural attention model is applied on two sequence classification tasks, dialogue act detection and key term extraction. In the sequence labeling tasks, the model input is a sequence, and the output is the label of the input sequence. The major difficulty of sequence labeling is that when the input sequence is long, it can include many noisy or irrelevant part. If the information in the whole sequence is treated equally, the noisy or irrelevant part may degrade the classification performance. The attention mechanism is helpful for sequence classification task because it is capable of highlighting important part among the entire sequence for the classification task. The experimental results show that with the attention mechanism, discernible improvements were achieved in the sequence labeling task considered here. The roles of the attention mechanism in the tasks are further analyzed and visualized in this paper.