# Simple and Effective Semi-Supervised Question Answering Source: [Simple and Effective Semi-Supervised Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:35.391Z Authors: Bhuwan Dhingra, Danish Pruthi, Dheeraj Rajagopal Published: 2018-04-02T20:29:21.000Z ## Abstract Recent success of deep learning models for the task of extractive Question Answering (QA) is hinged on the availability of large annotated corpora. However, large domain specific annotated corpora are limited and expensive to construct. In this work, we envision a system where the end user specifies a set of base documents and only a few labelled examples. Our system exploits the document structure to create cloze-style questions from these base documents; pre-trains a powerful neural network on the cloze style questions; and further fine-tunes the model on the labeled examples. We evaluate our proposed system across three diverse datasets from different domains, and find it to be highly effective with very little labeled data. We attain more than 50% F1 score on SQuAD and TriviaQA with less than a thousand labelled examples. We are also releasing a set of 3.2M cloze-style questions for practitioners to use while building QA systems. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Question Answering on SQuAD v1.1 (dev)](): Question Answering; SQuAD v1.1 (dev); F1 Score: 35.8. ## Original sources and implementation [Original paper]() [PDF]()