Field Extraction from Forms with Unlabeled Data
Mingfei Gao | Zeyuan Chen | Nikhil Naik | Kazuma Hashimoto | Caiming Xiong | Ran Xu
Proceedings of the 1st Workshop on Semiparametric Methods in NLP: Decoupling Logic from Knowledge
We propose a novel framework to conduct field extraction from forms with unlabeled data. To bootstrap the training process, we develop a rule-based method for mining noisy pseudo-labels from unlabeled forms. Using the supervisory signal from the pseudo-labels, we extract a discriminative token representation from a transformer-based model by modeling the interaction between text in the form. To prevent the model from overfitting to label noise, we introduce a refinement module based on a progressive pseudo-label ensemble. Experimental results demonstrate the effectiveness of our framework.