Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition
Table 4: Comparison results with the previous approaches and our approaches on SwDA dataset.
['models', 'Acc(%)']
In this section, we evaluate the proposed approaches on SwDA dataset. et al. pre-trained ELMo representationsPeters et al. However, in our work, we only applied the pre-trained word embedding. To illustrate the importance of context information, we also evaluate several sentence classification methods (CNN, LSTM, BERT) as baselines. For baseline models, both CNN and LSTM, got similar accuracy (75.27% and 75.59% respectively). We also fine-tuned BERT Devlin et al. As seen, with the powerful unsupervised pre-trained language model, BERT (76.88% accuracy) outperformed LSTM and CNN models for single sentence classification. However, it was still much lower than the models based on context information. It indicates that context information is crucial in the DA recognition task. BERT can boost performance in a large margin. However, it costs too much time and resources. In this reason, we chose LSTM as our utterance encoder in further experiment.