Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection
Table 1: SwDA dialogue act detection accuracies.
['[BOLD] Model (a) Support Vector Machine', '[BOLD] Model (a) Support Vector Machine', '[BOLD] Accuracy (%) 65.8']
Rows (a) to (d) are the baseline results, and the results of the proposed approaches are in rows (e) to (h). It is clear that the LSTM networks already outperformed the other baselines (rows (c) vs (a), (b)) because the LSTM networks have better capability of handling sequence information than multiple layer perceptrons and support vectors. Moreover, with context information the LSTM can have higher accuracy than the one without it ( rows (d) vs (c)).