# Applying Multilingual Models to Question Answering (QA)

Source: [Applying Multilingual Models to Question Answering (QA)](<https://www.sota2.com/research/paper/68fa6e1d1e209131cea3ddee>)

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

Data updated: 2025-10-26T00:49:11.447Z

Authors: Ayrton San Joaquin, Filip Skubacz

Published: 2022-12-04T21:58:33.000Z

## Abstract

We study the performance of monolingual and multilingual language models on the task of question-answering (QA) on three diverse languages: English, Finnish and Japanese. We develop models for the tasks of (1) determining if a question is answerable given the context and (2) identifying the answer texts within the context using IOB tagging. Furthermore, we attempt to evaluate the effectiveness of a pre-trained multilingual encoder (Multilingual BERT) on cross-language zero-shot learning for both the answerability and IOB sequence classifiers.

## 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/2212.01933>)

[PDF](<https://arxiv.org/pdf/2212.01933.pdf>)