Dual Attention Network for Product Compatibilityand Function Satisfiability Analysis
Table 5: Performance on product Function Satisfiability Analysis (FSA): FSA F1 is the averaged F1 scores over 3 types (satisfiable, unsatisfiable and uncertain) of extracted function expressions; FNR F1 only evaluates the performance of Function Need (function expression) Recognition without considering the polarity; Polarity Accuracy only evalutes the polarity detection given successfully identified function needs.
['Method', 'FSA F1', 'FNR F1', 'Polar. Acc.']
We then evaluate the performance of product function satisfiability analysis, which requires the label space of all models to be Ls={O, S, UN, U, F-S, F-UN, F-U}. Similar to the previous task, we consider an extracted function target that has more than or equal to 50% overlapping words with the ground truth function target as a positive function target extraction. If there is at least one function words that are correctly predicted, or the function word from the ground truth is missing, we consider such case as a positive function word extraction. A true positive extraction is generated when both a positive function target extraction and a positive function word extraction happen.