A Russian Jeopardy! Data Set for Question-Answering Systems

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1. Verfasser: Mikhalkova, Elena
Format: Preprint
Veröffentlicht: 2021
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author Mikhalkova, Elena
author_facet Mikhalkova, Elena
contents Question answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields. In industry, it is much appreciated in chatbots and corporate information systems. It is also a challenging task that attracted the attention of a very general audience at the quiz show Jeopardy! In this article we describe a Jeopardy!-like Russian QA data set collected from the official Russian quiz database Chgk (che ge ka). The data set includes 379,284 quiz-like questions with 29,375 from the Russian analogue of Jeopardy! - "Own Game". We observe its linguistic features and the related QA-task. We conclude about perspectives of a QA competition based on the data set collected from this database.
format Preprint
id arxiv_https___arxiv_org_abs_2112_02325
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle A Russian Jeopardy! Data Set for Question-Answering Systems
Mikhalkova, Elena
Computation and Language
Question answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields. In industry, it is much appreciated in chatbots and corporate information systems. It is also a challenging task that attracted the attention of a very general audience at the quiz show Jeopardy! In this article we describe a Jeopardy!-like Russian QA data set collected from the official Russian quiz database Chgk (che ge ka). The data set includes 379,284 quiz-like questions with 29,375 from the Russian analogue of Jeopardy! - "Own Game". We observe its linguistic features and the related QA-task. We conclude about perspectives of a QA competition based on the data set collected from this database.
title A Russian Jeopardy! Data Set for Question-Answering Systems
topic Computation and Language
url https://arxiv.org/abs/2112.02325