Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition
Table 5: Experiment results about the hyperparameter W and P on SwDA dataset and online prediction result. W,P indicate the size of sliding window and context padding length during training and testing.
['[ITALIC] W', '[ITALIC] P', 'models', 'Acc(%)']
To further illustrate the effect of the context length, we also performed experiments with different sliding It is worth noting that it is actually the same as single sentence classification when P=0 (without any context provided). First, we set W to 1 to discuss how the length of context padding will affect. As seen in the result, the accuracy increased when more context padding was used for both LSTM+BLSTM and LSTM+Attention approaches, so we did not evaluate the performance of LSTM+LC Attention when context padding is small. There was no further accuracy improvement when the length of context padding was beyond 5. Therefore, we fixed the context padding length P to 5 and increased the size of the sliding window to see how it works. With sliding window size increasing, the more context was involved together with more unnecessary information. From the experiments, we can see that both LSTM+BLSTM and LSTM+Attention achieved the best performance when window size was 1 and context padding length was 5. When window size increased, the performances of these two models dropped. However, our model (LSTM+LC Attention) can leverage the context information more efficiently, which achieved the best performance when window size was 10, and the model was more stable and robust to the different setting of window size. For online prediction, we only care about the recognition result of the last utterance in the given context. We added 5 preceding utterances as context padding for every predicted utterance because we cannot access subsequent utterances in the online setting. However, LSTM+LC Attention still outperformed the other two models.