# An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering

Source: [An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering](<https://www.sota2.com/research/paper/68fa66cd1e209131cea126a9>)

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

Data updated: 2025-10-25T21:59:21.344Z

Authors: Shayne Longpre, Yi Lu, Zhucheng Tu, Chris DuBois

Published: 2019-12-04T17:48:58.000Z

## Abstract

To produce a domain-agnostic question answering model for the Machine Reading Question Answering (MRQA) 2019 Shared Task, we investigate the relative benefits of large pre-trained language models, various data sampling strategies, as well as query and context paraphrases generated by back-translation. We find a simple negative sampling technique to be particularly effective, even though it is typically used for datasets that include unanswerable questions, such as SQuAD 2.0. When applied in conjunction with per-domain sampling, our XLNet (Yang et al., 2019)-based submission achieved the second best Exact Match and F1 in the MRQA leaderboard competition.

## 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/1912.02145>)

[PDF](<https://arxiv.org/pdf/1912.02145.pdf>)