# Towards More Equitable Question Answering Systems: How Much More Data Do You Need?

Source: [Towards More Equitable Question Answering Systems: How Much More Data Do You Need?](<https://www.sota2.com/research/paper/68fa6a0c1e209131cea25c48>)

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

Data updated: 2026-01-04T05:04:37.816Z

Authors: Arnab Debnath, Navid Rajabi, Fardina Fathmiul Alam, Antonios Anastasopoulos

Published: 2021-05-28T21:32:04.000Z

## Abstract

Question answering (QA) in English has been widely explored, but multilingual datasets are relatively new, with several methods attempting to bridge the gap between high- and low-resourced languages using data augmentation through translation and cross-lingual transfer. In this project, we take a step back and study which approaches allow us to take the most advantage of existing resources in order to produce QA systems in many languages. Specifically, we perform extensive analysis to measure the efficacy of few-shot approaches augmented with automatic translations and permutations of context-question-answer pairs. In addition, we make suggestions for future dataset development efforts that make better use of a fixed annotation budget, with a goal of increasing the language coverage of QA datasets and systems. Code and data for reproducing our experiments are available here: https://github.com/NavidRajabi/EMQA.

## 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/2105.14115>)

[PDF](<https://arxiv.org/pdf/2105.14115.pdf>)

[Implementation](<https://github.com/NavidRajabi/EMQA>)