Low-resource Deep Entity Resolution with Transfer and Active Learning
Table 3: Transfer learning results (citation genre). We report standard deviations of the F1 scores. For each target dataset, the source is given by the other two datasets (e.g., the source for DBLP-ACM is DBLP-Scholar and Cora.)
['Target Method', 'DBLP-ACM Prec', 'DBLP-ACM Recall', 'DBLP-ACM F1', 'DBLP-Scholar Prec', 'DBLP-Scholar Recall', 'DBLP-Scholar F1', 'Cora Prec', 'Cora Recall', 'Cora F1']
Transfer Learning. Our dataset adaptation method substantially ameliorates performance when the target is DBLP-Scholar (from 41.03 to 53.84 F1 points) or Cora (from 38.3 to 43.13 F1 points) and achieves the same level of performance on DBLP-ACM. Transfer learning with our dataset adaptation technique achieves a certain level of performance without any target labels, but we still observe high variance in performance (e.g. 6.21 standard deviation in DBLP-Scholar) and a huge discrepancy between transfer learning and training directly on the target dataset. To build a reliable and stable ER model, a certain amount of target labels may be necessary, which leads us to apply our active learning framework.