Table 3: Bot response categorisation
Table 3 shows that, in many cases, user repair led to relevant
responses. In practice, this means that user intent is often understood
after just one user repair and the conversation can progress. However,
in other cases user repair led to false negative responses (47) in which
the bot indicated a lack of understanding of user turns and often
explicitly requests repair. A less frequent category were false positive
responses (18). These are irrelevant turns which indicate that the bot
has misunderstood the user turn. In a few isolated instances, user
repair turns received no response at all (4), often prompting
self-initiated self-repair.
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 trouble source. In
one of them, the trouble source 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?