Sequence Segmentation using joint RNN and structured prediction models
Table 3: Proportion of differences between automatic and manual measures falling at or below a given tolerance value (in msec). These results are for the bb dataset.
['Model', '[ITALIC] t≤2', '[ITALIC] t≤5', '[ITALIC] t≤10', '[ITALIC] t≤15', '[ITALIC] t≤25', '[ITALIC] t≤50']
Bb For the bb dataset we use two layers of LSTMs with dropout after each recurrent layer. We have experiences with bidirectional LSTMs as well but only forward LSTM performs better on this dataset. We optimize the networks using AdaGrad optimization. All parameters were tuned on a dedicated development set. It is worth notice that we see the same behavior on this dataset as well, regarding the DeepVOT preforms better then the DeepSegmentor in hight tolerance values.