<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="belakova-gkatzia-2018-learning">
    <titleInfo>
        <title>Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jekaterina</namePart>
        <namePart type="family">Belakova</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dimitra</namePart>
        <namePart type="family">Gkatzia</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop on NLG for Human–Robot Interaction</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Mary</namePart>
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Foster</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hendrik</namePart>
            <namePart type="family">Buschmeier</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dimitra</namePart>
            <namePart type="family">Gkatzia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Tilburg, The Netherlands</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>One of the most natural ways for human robot communication is through spoken language. Training human-robot interaction systems require access to large datasets which are expensive to obtain and labour intensive. In this paper, we describe an approach for learning from minimal data, using as a toy example language understanding in spoken dialogue systems. Understanding of spoken language is crucial because it has implications for natural language generation, i.e. correctly understanding a user’s utterance will lead to choosing the right response/action. Finally, we discuss implications for Natural Language Generation in Human-Robot Interaction.</abstract>
    <identifier type="citekey">belakova-gkatzia-2018-learning</identifier>
    <identifier type="doi">10.18653/v1/W18-6902</identifier>
    <location>
        <url>https://aclanthology.org/W18-6902</url>
    </location>
    <part>
        <date>2018-11</date>
        <extent unit="page">
            <start>8</start>
            <end>11</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction
%A Belakova, Jekaterina
%A Gkatzia, Dimitra
%Y Foster, Mary Ellen
%Y Buschmeier, Hendrik
%Y Gkatzia, Dimitra
%S Proceedings of the Workshop on NLG for Human–Robot Interaction
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg, The Netherlands
%F belakova-gkatzia-2018-learning
%X One of the most natural ways for human robot communication is through spoken language. Training human-robot interaction systems require access to large datasets which are expensive to obtain and labour intensive. In this paper, we describe an approach for learning from minimal data, using as a toy example language understanding in spoken dialogue systems. Understanding of spoken language is crucial because it has implications for natural language generation, i.e. correctly understanding a user’s utterance will lead to choosing the right response/action. Finally, we discuss implications for Natural Language Generation in Human-Robot Interaction.
%R 10.18653/v1/W18-6902
%U https://aclanthology.org/W18-6902
%U https://doi.org/10.18653/v1/W18-6902
%P 8-11

Markdown (Informal)

[Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction](https://aclanthology.org/W18-6902) (Belakova & Gkatzia, INLG 2018)

• Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction (Belakova & Gkatzia, INLG 2018)

ACL

• Jekaterina Belakova and Dimitra Gkatzia. 2018. Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction. In Proceedings of the Workshop on NLG for Human–Robot Interaction, pages 8–11, Tilburg, The Netherlands. Association for Computational Linguistics.