Guiding attention in Sequence-to-sequence models for Dialogue Act prediction
Table 2: Accuracy of a seq2seq on dev test and BaselineCRF on SwDA and MRDA. Bold results exhibit significant differences (p-value<0.01) according to the Wilcoxon Mann Whitney test performed on 10 runs using different seeds.
['Models', 'SwDa', 'MRDA']
Current state of the art are built on CRF models. In this first section, we aim at comparing a seq2seq with a CRF based model. To provide a fair comparison we perform the same grid search for all models on a fixed grid. At this step, we do not use attention neither use beam search during training or inference. With an HGRU the seq2seq exhibit significantly higher results on SwDA and reaches comparable performances on MRDA. This behaviour suggests that a model based on a seq2seq architecture tends to be achieve higher score on DA classification than a CRF based model.