Detecting Interrogative Utteranceswith Recurrent Neural Networks
Table 4: Test F1 score of the models trained on MSR-Skype and tested on variable-length sequences.
['[EMPTY]', 'text,  [ITALIC] c2', 'audio,  [ITALIC] c2', 'combination,  [ITALIC] c2', 'condition,  [ITALIC] c2']
We use the same models that were trained on MSR-Skype, without any regularization methods. The sequences are divided into three groups depending on the number of words contained in each sequence. Short sequences have less than 5 words, long sequences have more than 20 words, and intermediate sequences contain 5 to 20 words. We observe that the models achieve the best performance on intermediate sequences, and the models tend to do better jobs on short and long sequences when the inputs contain text source. The performance degradations on short or long sequences compared to intermediate sequences are smaller when we use both input sources (see ‘combination’ and ‘condition’, especially models lose less performance against long sequences).