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?