<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="katsakioris-etal-2019-corpus">
    <titleInfo>
        <title>Corpus of Multimodal Interaction for Collaborative Planning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Miltiadis</namePart>
        <namePart type="given">Marios</namePart>
        <namePart type="family">Katsakioris</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Helen</namePart>
        <namePart type="family">Hastie</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ioannis</namePart>
        <namePart type="family">Konstas</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Atanas</namePart>
        <namePart type="family">Laskov</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Archna</namePart>
            <namePart type="family">Bhatia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yonatan</namePart>
            <namePart type="family">Bisk</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Parisa</namePart>
            <namePart type="family">Kordjamshidi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jesse</namePart>
            <namePart type="family">Thomason</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Minneapolis, Minnesota</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>As autonomous systems become more commonplace, we need a way to easily and naturally communicate to them our goals and collaboratively come up with a plan on how to achieve these goals. To this end, we conducted a Wizard of Oz study to gather data and investigate the way operators would collaboratively make plans via a conversational ‘planning assistant’ for remote autonomous systems. We present here a corpus of 22 dialogs from expert operators, which can be used to train such a system. Data analysis shows that multimodality is key to successful interaction, measured both quantitatively and qualitatively via user feedback.</abstract>
    <identifier type="citekey">katsakioris-etal-2019-corpus</identifier>
    <identifier type="doi">10.18653/v1/W19-1601</identifier>
    <location>
        <url>https://aclanthology.org/W19-1601</url>
    </location>
    <part>
        <date>2019-06</date>
        <extent unit="page">
            <start>1</start>
            <end>6</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Corpus of Multimodal Interaction for Collaborative Planning
%A Katsakioris, Miltiadis Marios
%A Hastie, Helen
%A Konstas, Ioannis
%A Laskov, Atanas
%Y Bhatia, Archna
%Y Bisk, Yonatan
%Y Kordjamshidi, Parisa
%Y Thomason, Jesse
%S Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F katsakioris-etal-2019-corpus
%X As autonomous systems become more commonplace, we need a way to easily and naturally communicate to them our goals and collaboratively come up with a plan on how to achieve these goals. To this end, we conducted a Wizard of Oz study to gather data and investigate the way operators would collaboratively make plans via a conversational ‘planning assistant’ for remote autonomous systems. We present here a corpus of 22 dialogs from expert operators, which can be used to train such a system. Data analysis shows that multimodality is key to successful interaction, measured both quantitatively and qualitatively via user feedback.
%R 10.18653/v1/W19-1601
%U https://aclanthology.org/W19-1601
%U https://doi.org/10.18653/v1/W19-1601
%P 1-6

Markdown (Informal)

[Corpus of Multimodal Interaction for Collaborative Planning](https://aclanthology.org/W19-1601) (Katsakioris et al., RoboNLP 2019)

• Corpus of Multimodal Interaction for Collaborative Planning (Katsakioris et al., RoboNLP 2019)

ACL

• Miltiadis Marios Katsakioris, Helen Hastie, Ioannis Konstas, and Atanas Laskov. 2019. Corpus of Multimodal Interaction for Collaborative Planning. In Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP), pages 1–6, Minneapolis, Minnesota. Association for Computational Linguistics.