EuSQuAD: Automatically Translated and Aligned SQuAD2.0 for Basque

Autores: Aitor García Pablos Naiara Pérez Montserrat Cuadros Oller Jaione Bengoetxea

Fecha: 01.09.2024

Procesamiento del Lenguaje Natural


Abstract

The widespread availability of Question Answering (QA) datasets in English has greatly facilitated the advancement of the Natural Language Processing (NLP) field. However, the scarcity of such resources for minority languages, such as Basque, poses a substantial challenge for these communities. In this context, the translation and alignment of existing QA datasets plays a crucial role in narrowing this technological gap. This work presents EuSQuAD, the first initiative dedicated to automatically translating and aligning SQuAD2.0 into Basque, resulting in more than 142k QA examples. We demonstrate EuSQuAD’s value through extensive qualitative analysis and QA experiments supported with EuSQuAD as training data. These experiments are evaluated with a new human-annotated dataset.

BIB_text

@Article {
title = {EuSQuAD: Automatically Translated and Aligned SQuAD2.0 for Basque},
journal = {Procesamiento del Lenguaje Natural},
pages = {125-137},
volume = {73},
keywds = {
Basque; question answering; reading comprehension; SQuAD
}
abstract = {

The widespread availability of Question Answering (QA) datasets in English has greatly facilitated the advancement of the Natural Language Processing (NLP) field. However, the scarcity of such resources for minority languages, such as Basque, poses a substantial challenge for these communities. In this context, the translation and alignment of existing QA datasets plays a crucial role in narrowing this technological gap. This work presents EuSQuAD, the first initiative dedicated to automatically translating and aligning SQuAD2.0 into Basque, resulting in more than 142k QA examples. We demonstrate EuSQuAD’s value through extensive qualitative analysis and QA experiments supported with EuSQuAD as training data. These experiments are evaluated with a new human-annotated dataset.


}
doi = {10.26342/2024-73-9},
date = {2024-09-01},
}
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