  The state-of-the-art performance on entity resolution (ER) has been achieved
by deep learning. However, deep models are usually trained on large quantities
of accurately labeled training data, and can not be easily tuned towards a
target workload. Unfortunately, in real scenarios, there may not be sufficient
labeled training data, and even worse, their distribution is usually more or
less different from the target workload even when they come from the same
domain.
  To alleviate the said limitations, this paper proposes a novel risk-based
approach to tune a deep model towards a target workload by its particular
characteristics. Built on the recent advances on risk analysis for ER, the
proposed approach first trains a deep model on labeled training data, and then
fine-tunes it by minimizing its estimated misprediction risk on unlabeled
target data. Our theoretical analysis shows that risk-based adaptive training
can correct the label status of a mispredicted instance with a fairly good
chance. We have also empirically validated the efficacy of the proposed
approach on real benchmark data by a comparative study. Our extensive
experiments show that it can considerably improve the performance of deep
models. Furthermore, in the scenario of distribution misalignment, it can
similarly outperform the state-of-the-art alternative of transfer learning by
considerable margins. Using ER as a test case, we demonstrate that risk-based
adaptive training is a promising approach potentially applicable to various
challenging classification tasks.
