# Improving Question Answering with Generation of NQ-like Questions

Source: [Improving Question Answering with Generation of NQ-like Questions](<https://www.sota2.com/research/paper/68fa6da01e209131cea3af47>)

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

Data updated: 2025-10-26T00:39:21.820Z

Authors: Saptarashmi Bandyopadhyay, Shraman Pal, Hao Zou, Abhranil Chandra, Jordan Boyd-Graber

Published: 2022-10-12T21:36:20.000Z

## Abstract

Question Answering (QA) systems require a large amount of annotated data which is costly and time-consuming to gather. Converting datasets of existing QA benchmarks are challenging due to different formats and complexities. To address these issues, we propose an algorithm to automatically generate shorter questions resembling day-to-day human communication in the Natural Questions (NQ) dataset from longer trivia questions in Quizbowl (QB) dataset by leveraging conversion in style among the datasets. This provides an automated way to generate more data for our QA systems. To ensure quality as well as quantity of data, we detect and remove ill-formed questions using a neural classifier. We demonstrate that in a low resource setting, using the generated data improves the QA performance over the baseline system on both NQ and QB data. Our algorithm improves the scalability of training data while maintaining quality of data for 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.06599>)

[PDF](<https://arxiv.org/pdf/2210.06599.pdf>)