Deriving Machine Attention from Human Rationales
Table 4: Accuracy of transferring between domains. Models with † use labeled data from source domains and unlabeled data from the target domain. Models with ‡ use human rationales on the target task.
['Source', 'Target', 'Svm', 'Ra-Svm‡', 'Ra-Cnn‡', 'Trans†', 'Ra-Trans‡†', 'Ours‡†', 'Oracle†']
Table 4 presents the results of domain transfer using 200 training examples. We use the three aspects of the beer review data together as our source tasks while use the three aspects of hotel review data as the target. Our model (OURS) shows marked performance improvement. The error reduction over the best baseline is 15.08% on average.