@inproceedings{williams-scheutz-2017-referring,
title = "Referring Expression Generation under Uncertainty: Algorithm and Evaluation Framework",
author = "Williams, Tom and
Scheutz, Matthias",
editor = "Alonso, Jose M. and
Bugar{\'\i}n, Alberto and
Reiter, Ehud",
booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
month = sep,
year = "2017",
address = "Santiago de Compostela, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-3511",
doi = "10.18653/v1/W17-3511",
pages = "75--84",
abstract = "For situated agents to effectively engage in natural-language interactions with humans, they must be able to refer to entities such as people, locations, and objects. While classic referring expression generation (REG) algorithms like the Incremental Algorithm (IA) assume perfect, complete, and accessible knowledge of all referents, this is not always possible. In this work, we show how a previously presented consultant framework (which facilitates reference resolution when knowledge is uncertain, heterogeneous and distributed) can be used to extend the IA to produce DIST-PIA, a domain-independent algorithm for REG under uncertain, heterogeneous, and distributed knowledge. We also present a novel framework that can be used to evaluate such REG algorithms without conflating the performance of the algorithm with the performance of classifiers it employs.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="williams-scheutz-2017-referring">
<titleInfo>
<title>Referring Expression Generation under Uncertainty: Algorithm and Evaluation Framework</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tom</namePart>
<namePart type="family">Williams</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Matthias</namePart>
<namePart type="family">Scheutz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 10th International Conference on Natural Language Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jose</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Alonso</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alberto</namePart>
<namePart type="family">Bugarín</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ehud</namePart>
<namePart type="family">Reiter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Santiago de Compostela, Spain</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>For situated agents to effectively engage in natural-language interactions with humans, they must be able to refer to entities such as people, locations, and objects. While classic referring expression generation (REG) algorithms like the Incremental Algorithm (IA) assume perfect, complete, and accessible knowledge of all referents, this is not always possible. In this work, we show how a previously presented consultant framework (which facilitates reference resolution when knowledge is uncertain, heterogeneous and distributed) can be used to extend the IA to produce DIST-PIA, a domain-independent algorithm for REG under uncertain, heterogeneous, and distributed knowledge. We also present a novel framework that can be used to evaluate such REG algorithms without conflating the performance of the algorithm with the performance of classifiers it employs.</abstract>
<identifier type="citekey">williams-scheutz-2017-referring</identifier>
<identifier type="doi">10.18653/v1/W17-3511</identifier>
<location>
<url>https://aclanthology.org/W17-3511</url>
</location>
<part>
<date>2017-09</date>
<extent unit="page">
<start>75</start>
<end>84</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Referring Expression Generation under Uncertainty: Algorithm and Evaluation Framework
%A Williams, Tom
%A Scheutz, Matthias
%Y Alonso, Jose M.
%Y Bugarín, Alberto
%Y Reiter, Ehud
%S Proceedings of the 10th International Conference on Natural Language Generation
%D 2017
%8 September
%I Association for Computational Linguistics
%C Santiago de Compostela, Spain
%F williams-scheutz-2017-referring
%X For situated agents to effectively engage in natural-language interactions with humans, they must be able to refer to entities such as people, locations, and objects. While classic referring expression generation (REG) algorithms like the Incremental Algorithm (IA) assume perfect, complete, and accessible knowledge of all referents, this is not always possible. In this work, we show how a previously presented consultant framework (which facilitates reference resolution when knowledge is uncertain, heterogeneous and distributed) can be used to extend the IA to produce DIST-PIA, a domain-independent algorithm for REG under uncertain, heterogeneous, and distributed knowledge. We also present a novel framework that can be used to evaluate such REG algorithms without conflating the performance of the algorithm with the performance of classifiers it employs.
%R 10.18653/v1/W17-3511
%U https://aclanthology.org/W17-3511
%U https://doi.org/10.18653/v1/W17-3511
%P 75-84
Markdown (Informal)
[Referring Expression Generation under Uncertainty: Algorithm and Evaluation Framework](https://aclanthology.org/W17-3511) (Williams & Scheutz, INLG 2017)
ACL