DaNetQA: a yes/no Question Answering Dataset for the Russian Language
Abstract
DaNetQA, a new question-answering corpus, follows (Clark et. al, 2019) design: it comprises natural yes/no questions. Each question is paired with a paragraph from Wikipedia and an answer, derived from the paragraph. The task is to take both the question and a paragraph as input and come up with a yes/no answer, i.e. to produce a binary output. In this paper, we present a reproducible approach to DaNetQA creation and investigate transfer learning methods for task and language transferring. For task transferring we leverage three similar sentence modelling tasks: 1) a corpus of paraphrases, Paraphraser, 2) an NLI task, for which we use the Russian part of XNLI, 3) another question answering task, SberQUAD. For language transferring we use English to Russian translation together with multilingual language fine-tuning.
Keywords
Cite
@article{arxiv.2010.02605,
title = {DaNetQA: a yes/no Question Answering Dataset for the Russian Language},
author = {Taisia Glushkova and Alexey Machnev and Alena Fenogenova and Tatiana Shavrina and Ekaterina Artemova and Dmitry I. Ignatov},
journal= {arXiv preprint arXiv:2010.02605},
year = {2023}
}
Comments
Analysis of Images, Social Networks and Texts - 9 th International Conference, AIST 2020, Skolkovo, Russia, October 15-16, 2020, Revised Selected Papers. Lecture Notes in Computer Science (https://dblp.org/db/series/lncs/index.html), Springer 2020