Low-resource Deep Entity Resolution with Transfer and Active Learning
Table 5: Transfer and active learning results in the restaurant genre. The target and source datasets are Fodors-Zagats and Zomato-Yelp respectively.
['Method', 'Prec', 'Recall', 'F1']
Other Genre Results. We present results from the restaurant and software genres. Similarly to our extensive experiments in the citation genre, the dataset adaptation technique facilitates transfer learning significantly, and only 100 active learning labels are needed to achieve the same performance as the model trained with all target labels (894 labels). Fig. The relative performance among the 6 non-DL approaches differs to a great degree as the best non-DL model is now logistic regression, but deep active learning outperforms the rest with 1200 labeled examples (10.4% of training data). These results illustrate that our low-resource frameworks are effective in other genres as well.