# Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages

Source: [Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages](<https://www.sota2.com/research/paper/68fa74af1e209131cea633c4>)

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

Data updated: 2025-10-26T03:16:29.756Z

Authors: Hariom A. Pandya, Bhavik Ardeshna, Brijesh S. Bhatt

Published: 2021-12-18T07:40:37.000Z

## Abstract

Transformer based architectures have shown notable results on many down streaming tasks including question answering. The availability of data, on the other hand, impedes obtaining legitimate performance for low-resource languages. In this paper, we investigate the applicability of pre-trained multilingual models to improve the performance of question answering in low-resource languages. We tested four combinations of language and task adapters using multilingual transformer architectures on seven languages similar to MLQA dataset. Additionally, we have also proposed zero-shot transfer learning of low-resource question answering using language and task adapters. We observed that stacking the language and the task adapters improves the multilingual transformer models' performance significantly for low-resource languages.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2112.09866>)

[PDF](<https://arxiv.org/pdf/2112.09866.pdf>)