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Extracting structured information from HTML documents is a long-studiedproblem with a broad range of applications, including knowledge baseconstruction, faceted search, and personalized recommendation. Prior works relyon a few human-labeled web pages from each target website or thousands ofhuman-labeled web pages from some seed websites to train a transferableextraction model that generalizes on unseen target websites. Noisy content, lowsite-level consistency, and lack of inter-annotator agreement make labeling webpages a time-consuming and expensive ordeal. We develop LEAST -- aLabel-Efficient Self-Training method for Semi-Structured Web Documents toovercome these limitations. LEAST utilizes a few human-labeled pages topseudo-annotate a large number of unlabeled web pages from the target vertical.It trains a transferable web-extraction model on both human-labeled andpseudo-labeled samples using self-training. To mitigate error propagation dueto noisy training samples, LEAST re-weights each training sample based on itsestimated label accuracy and incorporates it in training. To the best of ourknowledge, this is the first work to propose end-to-end training fortransferable web extraction models utilizing only a few human-labeled pages.Experiments on a large-scale public dataset show that using less than tenhuman-labeled pages from each seed website for training, a LEAST-trained modeloutperforms previous state-of-the-art by more than 26 average F1 points onunseen websites, reducing the number of human-labeled pages to achieve similarperformance by more than 10x.

Label-Efficient Self-Training for Attribute Extraction from Semi-Structured Web Documents