Table 4: Bot response categorisation
Table 4 shows that the majority of user repairs lead to relevant
responses which progress the conversation. However, a significant number
of user repair leads to false negative responses. These initiate user
repair by indicating a lack of understanding of user turns. A less
frequent category are false positive responses. These are irrelevant
turns which initiate repair by displaying that the user turn has been
misunderstood. In a few isolated instances, user repair turns receive no
response at all.
Successful and unsuccessful repair
As stated above, the main objective of his study was to investigate how
users deploy conversational repair to navigate through episodes in which
the bot lacks understanding of or misunderstands their intents. In the
forthcoming section, I will present, as case study, two sets of paired
examples. Each of these pairs starts from a similar starting point
(trouble source). In one of them, the trouble sourced was dealt with
easily to allow the conversation to progress. In the other, users faced
more difficulties in addressing the misunderstanding.
The first two examples start from a user question as trouble source:
Example (1)
1 Is an MRI scan harmful?