Predicting Causes of Reformulation in Intelligent Assistants
Table 10: F1-measure in reformulation cause prediction for each label.
['[EMPTY]', '[BOLD] No', '[BOLD] ASR', '[BOLD] NLU', '[BOLD] LG']
Again, the proposed method outperforms the other methods. Focusing on individual labels, F1-measures of the proposed method are better for No error and ASR error than for NLU error and LG error. In other words, F1-measures for NLU error and LG error are not high. As the performances of individual labels are in the order of the number of samples belonging to the label, we expect that these low performances are mainly due to insufficient data and will improve given sufficient data. Focusing on individual feature sets, ASR and NLU features are useful for distinguishing No error from other labels. Note that statistical significant differences from the baseline detected by the paired t-test are denoted by + (p<0.01) and ⋆ (p<0.05).