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?