# Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering

Source: [Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering](<https://www.sota2.com/research/paper/68fa67731e209131cea165df>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:15:13.389Z

Authors: Alexander R. Fabbri, Patrick Ng, Zhiguo Wang, Ramesh Nallapati, Bing Xiang

Published: 2020-04-24T17:57:45.000Z

## Abstract

Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has been to fine-tune a pretrained language model on a task-specific labeled dataset. This paradigm, however, relies on scarce, and costly to obtain, large-scale human-labeled data. We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance by allowing the model to learn more complex context-question relationships. Training a QA model on this data gives a relative improvement over a previous unsupervised model in F1 score on the SQuAD dataset by about 14%, and 20% when the answer is a named entity, achieving state-of-the-art performance on SQuAD for unsupervised QA.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.11892>)

[PDF](<https://arxiv.org/pdf/2004.11892.pdf>)