Predicting Causes of Reformulation in Intelligent Assistants
Table 4: Distribution of correction types (%).
['[EMPTY]', '[BOLD] ADD', '[BOLD] OMIT', '[BOLD] PAR', '[BOLD] OTHER']
In No error, the percentages of ADD and PAR are large. We can also see that NLU error and LG error have similar distributions and that OMIT is more frequent than the other correction types. This is because when users encounter NLU error or LG error, they use different types of corrections depending on the situation: adding words to clarify their intent, omitting words that seem to be the noise for the system, and so on. We expect correction types to be useful for detecting reformulation causes as the distribution of correction types differs for different error types.