Finding Dominant User Utterances And System Responses in Conversations
Table 1: Performance of SimCluster versus K-means clustering on synthetic dataset
['[EMPTY]', 'Domain 1 F1-score', 'Domain 1 ARI', 'Domain 2 F1-score', 'Domain 2 ARI']
We used the gaussian index from which an utterance pair was generated as the ground truth label, which served to provide ground truth clusters for computation of the above evaluation metrics. versus K-means algorithm. Here our SimCluster algorithm improves the F1-scores from 0.412 and 0.417 in the two domains to 0.442 and 0.441. The ARI scores also improve from 0.176 and 0.180 to 0.203 and 0.204.