Sequence Segmentation using joint RNN and structured prediction models
Table 2: Proportion of differences between automatic and manual measures falling at or below a given tolerance value (in msec). For example, for DeepVOT, the difference between automatic and manual measurements in the test set was 2 msec or less in 53.8% of examples. These results are for the pgwords dataset.
['Model', '[ITALIC] t≤2', '[ITALIC] t≤5', '[ITALIC] t≤10', '[ITALIC] t≤15', '[ITALIC] t≤25', '[ITALIC] t≤50']
Pgwords For the pgwords dataset we use two layers of bidirectional LSTMs with dropout after each recurrent layer. We optimize the networks using AdaGrad optimization. All parameters were tuned on a dedicated development set. Results suggests that DeepSegmentor outperforms the AutoVOT model over all tolerance values. However, when comparing to DeepVOT, the picture is mixed. In the lower tolerance values DeepSegmentor is superior to the DeepVOT while for higher values DeepVOT performs better. We believe these results are due to the DeepVOT being less delicate and solving a much coarser problem than the DeepSegmentor ; hence, it performs better when considering high tolerance values. We believe the integration between these two systems, (using DeepVOT as pre-training for the DeepSegmentor ), will yield more accurate and robust results. We leave this investigation for future work.