Baran: Effective Error Correction via a Unified Context Representation and Transfer Learning

Traditional error correction solutions leverage handmaid rules or master data to find the correct values. Both are often amiss in real-world scenarios. Therefore, it is desirable to additionally learn corrections from a limited number of example repairs. To effectively generalize example repairs, it is necessary to capture the entire context of each erroneous value. A context comprises the value itself, the co-occurring values inside the same tuple, and all values that define the attribute type. Typically, an error corrector based on any of these context information undergoes an individual process of operations that is not always easy to integrate with other types of error correctors. In this paper, we present a new error correction system, Baran, which provides a unifying abstraction for integrating multiple error corrector models that can be pretrained and updated in the same way. Because of the holistic nature of our approach, we generate more correction candidates than state of the art and, because of the underlying context-aware data representation, we achieve high precision. We show that, by pretraining our models based on Wikipedia revisions, our system can further improve its overall precision and recall. In our experiments, Baran significantly outperforms state-of-the-art error correction systems in terms of effectiveness and human involvement requiring only 20 labeled tuples.