Neural-based Context Representation Learning for Dialog Act Classification
Table 6: Comparison of accuracy (%). CNN-FF and LSTM-FF: proposed in Lee and Dernoncourt (2016), HBM: hidden backoff model (Ji and Bilmes, 2006). LV-RNN: latent variable RNN with conditional training (Ji et al., 2016). HCNN: hierarchical CNN (Kalchbrenner and Blunsom, 2013). CA-LSTM: contextual attentive LSTM (Shen and Lee, 2016). HMM Stolcke et al. (2000).
['[BOLD] Model', '[BOLD] MRDA', '[BOLD] SwDA']
To the best of our knowledge, Lee and Dernoncourt Therefore, an accurate comparison of our results can be only done with this work. Our model yields comparable results to the state-of-the-art on both datasets, 84.3% against 84.6% on MRDA and 73.8% against 73.1% on SwDA. Ji et al.