# DaNetQA: a yes/no Question Answering Dataset for the Russian Language

Source: [DaNetQA: a yes/no Question Answering Dataset for the Russian Language](<https://www.sota2.com/research/paper/68fa70b71e209131cea4cc94>)

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

Data updated: 2025-10-26T01:48:53.779Z

Authors: Taisia Glushkova, Alexey Machnev, Alena Fenogenova, Tatiana Shavrina, Ekaterina Artemova, Dmitry I. Ignatov

Published: 2020-10-06T10:30:48.000Z

## 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.

## 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/2010.02605>)

[PDF](<https://arxiv.org/pdf/2010.02605.pdf>)