<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mikhalkova-khlyupin-2022-russian">
    <titleInfo>
        <title>Russian Jeopardy! Data Set for Question-Answering Systems</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Elena</namePart>
        <namePart type="family">Mikhalkova</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="given">A</namePart>
        <namePart type="family">Khlyupin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Thirteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <originInfo>
            <publisher>European Language Resources Association</publisher>
            <place>
                <placeTerm type="text">Marseille, France</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>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 valued in chat-bots 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 Ch-g-k. 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 challenge based on the collected data set.</abstract>
    <identifier type="citekey">mikhalkova-khlyupin-2022-russian</identifier>
    <location>
        <url>https://aclanthology.org/2022.lrec-1.53</url>
    </location>
    <part>
        <date>2022-06</date>
        <extent unit="page">
            <start>508</start>
            <end>514</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Russian Jeopardy! Data Set for Question-Answering Systems
%A Mikhalkova, Elena
%A Khlyupin, Alexander A.
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F mikhalkova-khlyupin-2022-russian
%X 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 valued in chat-bots 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 Ch-g-k. 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 challenge based on the collected data set.
%U https://aclanthology.org/2022.lrec-1.53
%P 508-514

Markdown (Informal)

[Russian Jeopardy! Data Set for Question-Answering Systems](https://aclanthology.org/2022.lrec-1.53) (Mikhalkova & Khlyupin, LREC 2022)

• Russian Jeopardy! Data Set for Question-Answering Systems (Mikhalkova & Khlyupin, LREC 2022)

ACL

• Elena Mikhalkova and Alexander A. Khlyupin. 2022. Russian Jeopardy! Data Set for Question-Answering Systems. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 508–514, Marseille, France. European Language Resources Association.