Dialogue Act Sequence Labeling using Hierarchical encoder with CRF
Table 7: Comparing Accuracy of our method (Bi-LSTM-CRF) with other methods in the literature on the MRDA dataset.
['[BOLD] Model', '[BOLD] Acc(%)']
From this table, it is clear that our method outperforms the state-of-the-art by a significant margin i.e. by 4.1%. Except for the class label ’B’, all other DA class labels are predicted accurately. Approximately 21% of DA class label ’B’ are incorrectly predicted as ’S’. One of the reasons for this behavior is that the MRDA dataset is highly imbalanced, with more than 50% of the utterances labeled as class ’S’.