Low-resource Deep Entity Resolution with Transfer and Active Learning
Table 4: Low-resource (shaded) and high-resource (full training data) performance comparison. DTAL, DAL, and DL denote deep transfer active learning, deep active learning, and deep learning (random sampling).
['Dataset', 'Method', 'Train Size', 'F1']
(We only show the SVM results since SVM performed best in each configuration among the 6 non-DL algorithms.) First, deep transfer active learning (DTAL) achieves the best performance in the low-resource setting of each dataset. In particular, DTAL outperforms the others to the greatest degree in Cora (97.68 F1 points) probably because Cora is the most complex dataset with 8 attributes in the schema. Non-DL algorithms require many interaction features, which lead to data sparsity. Deep active learning (DAL) also outperforms SVM and yields comparable performance to DTAL. However, the standard deviations in performance of DAL are substantially higher than those of DTAL (e.g. 4.15 vs. 0.33 in DBLP-ACM), suggesting that transfer learning provides useful initializations for active learning to achieve stable performance.