<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yoneda-etal-2018-ucl">
    <titleInfo>
        <title>UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Takuma</namePart>
        <namePart type="family">Yoneda</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jeff</namePart>
        <namePart type="family">Mitchell</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Johannes</namePart>
        <namePart type="family">Welbl</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Pontus</namePart>
        <namePart type="family">Stenetorp</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sebastian</namePart>
        <namePart type="family">Riedel</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 First Workshop on Fact Extraction and VERification (FEVER)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">James</namePart>
            <namePart type="family">Thorne</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Oana</namePart>
            <namePart type="family">Cocarascu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Arpit</namePart>
            <namePart type="family">Mittal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Brussels, Belgium</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper we describe our 2nd place FEVER shared-task system that achieved a FEVER score of 62.52% on the provisional test set (without additional human evaluation), and 65.41% on the development set. Our system is a four stage model consisting of document retrieval, sentence retrieval, natural language inference and aggregation. Retrieval is performed leveraging task-specific features, and then a natural language inference model takes each of the retrieved sentences paired with the claimed fact. The resulting predictions are aggregated across retrieved sentences with a Multi-Layer Perceptron, and re-ranked corresponding to the final prediction.</abstract>
    <identifier type="citekey">yoneda-etal-2018-ucl</identifier>
    <identifier type="doi">10.18653/v1/W18-5515</identifier>
    <location>
        <url>https://aclanthology.org/W18-5515</url>
    </location>
    <part>
        <date>2018-11</date>
        <extent unit="page">
            <start>97</start>
            <end>102</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)
%A Yoneda, Takuma
%A Mitchell, Jeff
%A Welbl, Johannes
%A Stenetorp, Pontus
%A Riedel, Sebastian
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F yoneda-etal-2018-ucl
%X In this paper we describe our 2nd place FEVER shared-task system that achieved a FEVER score of 62.52% on the provisional test set (without additional human evaluation), and 65.41% on the development set. Our system is a four stage model consisting of document retrieval, sentence retrieval, natural language inference and aggregation. Retrieval is performed leveraging task-specific features, and then a natural language inference model takes each of the retrieved sentences paired with the claimed fact. The resulting predictions are aggregated across retrieved sentences with a Multi-Layer Perceptron, and re-ranked corresponding to the final prediction.
%R 10.18653/v1/W18-5515
%U https://aclanthology.org/W18-5515
%U https://doi.org/10.18653/v1/W18-5515
%P 97-102

Markdown (Informal)

[UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)](https://aclanthology.org/W18-5515) (Yoneda et al., EMNLP 2018)

• UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF) (Yoneda et al., EMNLP 2018)

ACL

• Takuma Yoneda, Jeff Mitchell, Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018. UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF). In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 97–102, Brussels, Belgium. Association for Computational Linguistics.