Paying Attention to Attention: Highlighting Influential Samples in Sequential Analysis
Table 4: The influence on escalation of each user turn in two sample conversations where 4 weight values are possible ({0,13,23,1}).
['[BOLD] Turn', '[BOLD] User Text', '[BOLD] Han Weight', '[BOLD] Our Weight']
Compute ¯vi=(μi+βi+γi)/3, and use this value to represent the intensity of a single color (blue in our examples). The higher the value of ¯vi, the higher the color intensity. Note that ¯vi∈{0,13,23,1}. ’s weights (using our weights), turns 2,3, and 6 have values of 23, turns 4,5, and 7 have values of 13, and the first turn has a value of 0. Considering a higher dimension for vi which would create more values for ¯vi is an objective for future work. In cases of uniform attention weights at the stopping point, this can also make it difficult to spot potential areas for classifier improvement if we do not incorporate turn weight fluctuations as the conversation progresses to the stopping point. Customers will frequently ask for a “live customer representative” which is a sign for escalation. Our weights expose this potential problem for the classifier which may suggest using n-grams or some other methodology for improvement. If we were to use uniform Han weights at the stopping point only, we might miss these areas for improvement.