# Simple and Effective Semi-Supervised Question Answering

arXiv: https://arxiv.org/abs/1804.00720 · HTML: https://mlsanity.com/paper/1804.00720/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2018.

## Built on by (most cited first, 22 of 22)

- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [Towards Mitigating Hallucination in Large Language Models via Self-Reflection](https://mlsanity.com/paper/2310.06271/) (2023)
- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [Self-Supervised Test-Time Learning for Reading Comprehension](https://mlsanity.com/paper/2103.11263/) (2021)
- [Contrastive Domain Adaptation for Question Answering using Limited Text Corpora](https://mlsanity.com/paper/2108.13854/) (2021)
- [LIQUID: A Framework for List Question Answering Dataset Generation](https://mlsanity.com/paper/2302.01691/) (2023)
- [Generating Information-Seeking Conversations from Unlabeled Documents](https://mlsanity.com/paper/2205.12609/) (2022)
- [Synthetic Question Value Estimation for Domain Adaptation of Question Answering](https://mlsanity.com/paper/2203.08926/) (2022)
- [Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges](https://mlsanity.com/paper/2309.12426/) (2023)
- [Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlap](https://mlsanity.com/paper/2109.11256/) (2021)
- [Relation-Guided Pre-Training for Open-Domain Question Answering](https://mlsanity.com/paper/2109.10346/) (2021)
- [Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data](https://mlsanity.com/paper/2102.01226/) (2021)
- [Improving Question Answering Performance Using Knowledge Distillation and Active Learning](https://mlsanity.com/paper/2109.12662/) (2021)
- [Unsupervised Natural Language Inference Using PHL Triplet Generation](https://mlsanity.com/paper/2110.08438/) (2021)
- [Synthetic Target Domain Supervision for Open Retrieval QA](https://mlsanity.com/paper/2204.09248/) (2022)
- [Data Augmentation for Biomedical Factoid Question Answering](https://mlsanity.com/paper/2204.04711/) (2022)
- [Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision](https://mlsanity.com/paper/2107.08582/) (2021)
- [Momentum Contrastive Pre-training for Question Answering](https://mlsanity.com/paper/2212.05762/) (2022)
- [Interviewer-Candidate Role Play: Towards Developing Real-World NLP Systems](https://mlsanity.com/paper/2107.00315/) (2021)
- [Recent Advances in Automated Question Answering In Biomedical Domain](https://mlsanity.com/paper/2111.05937/) (2021)
- [Addressing Data Scarcity in Multimodal User State Recognition by Combining Semi-Supervised and Supervised Learning](https://mlsanity.com/paper/2202.03775/) (2022)
- [Perturbation-based Active Learning for Question Answering](https://mlsanity.com/paper/2311.02345/) (2023)

## Abstract (arXiv)

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.