# Harvesting and Refining Question-Answer Pairs for Unsupervised QA

arXiv: https://arxiv.org/abs/2005.02925 · HTML: https://mlsanity.com/paper/2005.02925/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 20 of 20)

- [Self-Supervised Test-Time Learning for Reading Comprehension](https://mlsanity.com/paper/2103.11263/) (2021)
- [Improving Unsupervised Question Answering via Summarization-Informed Question Generation](https://mlsanity.com/paper/2109.07954/) (2021)
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- [Relation-Guided Pre-Training for Open-Domain Question Answering](https://mlsanity.com/paper/2109.10346/) (2021)
- [Test-Time Self-Adaptive Small Language Models for Question Answering](https://mlsanity.com/paper/2310.13307/) (2023)
- [QA Is the New KR: Question-Answer Pairs as Knowledge Bases](https://mlsanity.com/paper/2207.00630/) (2022)
- [C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References](https://mlsanity.com/paper/2203.08928/) (2022)
- [Unsupervised Question Answering via Answer Diversifying](https://mlsanity.com/paper/2208.10813/) (2022)
- [QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering](https://mlsanity.com/paper/2310.11303/) (2023)
- [Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision](https://mlsanity.com/paper/2107.08582/) (2021)
- [AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking](https://mlsanity.com/paper/2305.02235/) (2023)
- [Universal Model in Online Customer Service](https://mlsanity.com/paper/2402.15666/) (2024)
- [Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction](https://mlsanity.com/paper/2105.12392/) (2021)
- [Momentum Contrastive Pre-training for Question Answering](https://mlsanity.com/paper/2212.05762/) (2022)
- [PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora](https://mlsanity.com/paper/2301.01064/) (2023)
- [Unsupervised Open-Domain Question Answering](https://mlsanity.com/paper/2108.13817/) (2021)
- [Weakly Supervised Visual Question Answer Generation](https://mlsanity.com/paper/2306.06622/) (2023)
- [Unsupervised multiple choices question answering via universal corpus](https://mlsanity.com/paper/2402.17333/) (2024)

## Abstract (arXiv)

Question Answering (QA) has shown great success thanks to the availability of large-scale datasets and the effectiveness of neural models. Recent research works have attempted to extend these successes to the settings with few or no labeled data available. In this work, we introduce two approaches to improve unsupervised QA. First, we harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a corpus of question-answer pairs (named as RefQA). Second, we take advantage of the QA model to extract more appropriate answers, which iteratively refines data over RefQA. We conduct experiments on SQuAD 1.1, and NewsQA by fine-tuning BERT without access to manually annotated data. Our approach outperforms previous unsupervised approaches by a large margin and is competitive with early supervised models. We also show the effectiveness of our approach in the few-shot learning setting.