Guiding attention in Sequence-to-sequence models for Dialogue Act prediction
Table 6: Accuracy of our best models (seq2seq) and BaselineCRF on SwDA and MRDA test sets.
['Models', 'SwDa', 'MRDA']
In this section, we compare the performances of seq2seqBEST with other state of the art models and analyse the performances of the models. seq2seqBEST achieves an accuracy of 85% on the SwDA corpora. This model outperforms \citeauthorLSTM_CRF \shortciteLSTM_CRF and \citeauthorDAClassifCxt \shortciteDAClassifCxt which achieve an accuracy of 82.9%. On MRDA, our best performing model reaches an accuracy of 91.6% where current state-of-the-art systems, \citeauthorLSTM_CRF,hierarchical_LSTM_CRF \shortciteLSTM_CRF,hierarchical_LSTM_CRF achieve respectively 92.2% and 91.7%.