Lexico-acoustic Neural-based Models for Dialog Act Classification
Table 4: Accuracy (%) of Question utterances on MRDA with question mark and when the question mark is removed.
['[BOLD] Model', '[BOLD] With  [ITALIC] ’?’', '[ITALIC]  [BOLD] ’?’ removed']
Effect of removing the question mark Contrary to our initial hypothesis that acoustic features would improve the accuracy of classifying Question , no improvement was noted. Therefore, we analyzed how the LM and the LAM performed on this particular DA more deeply. The question mark ? For that reason, the acoustic features are not able to provide any useful information. The second column corresponds to the models which were trained and tested on the original transcripts and the third column to the models which were trained on transcripts with question marks removed. As mentioned above, the LM has a high accuracy at correctly predicting Question if the utterance has the question mark. Moreover, when the acoustic features are added, the accuracy decreases by 1.6%. Nonetheless, if question marks are not present in the data, the LM’s accuracy drops to 46.6%. This shows that this character is the most important cue at lexical level. The LAM’s accuracy drops to 50.2%, but this time it is superior to the LM by 3.6%. This indicates that acoustic information is an important source of cues for tasks that use DA classification over data that lacks these important lexical cues, such as spoken language understanding.