<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="honovich-etal-2020-machine">
    <titleInfo>
        <title>Machine Reading of Historical Events</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Or</namePart>
        <namePart type="family">Honovich</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lucas</namePart>
        <namePart type="family">Torroba Hennigen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Omri</namePart>
        <namePart type="family">Abend</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shay</namePart>
        <namePart type="given">B</namePart>
        <namePart type="family">Cohen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joyce</namePart>
            <namePart type="family">Chai</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Natalie</namePart>
            <namePart type="family">Schluter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities. Within this broad framework, we address the task of machine reading the time of historical events, compile datasets for the task, and develop a model for tackling it. Given a brief textual description of an event, we show that good performance can be achieved by extracting relevant sentences from Wikipedia, and applying a combination of task-specific and general-purpose feature embeddings for the classification. Furthermore, we establish a link between the historical event ordering task and the event focus time task from the information retrieval literature, showing they also provide a challenging test case for machine reading algorithms.</abstract>
    <identifier type="citekey">honovich-etal-2020-machine</identifier>
    <identifier type="doi">10.18653/v1/2020.acl-main.668</identifier>
    <location>
        <url>https://aclanthology.org/2020.acl-main.668</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>7486</start>
            <end>7497</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Machine Reading of Historical Events
%A Honovich, Or
%A Torroba Hennigen, Lucas
%A Abend, Omri
%A Cohen, Shay B.
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F honovich-etal-2020-machine
%X Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities. Within this broad framework, we address the task of machine reading the time of historical events, compile datasets for the task, and develop a model for tackling it. Given a brief textual description of an event, we show that good performance can be achieved by extracting relevant sentences from Wikipedia, and applying a combination of task-specific and general-purpose feature embeddings for the classification. Furthermore, we establish a link between the historical event ordering task and the event focus time task from the information retrieval literature, showing they also provide a challenging test case for machine reading algorithms.
%R 10.18653/v1/2020.acl-main.668
%U https://aclanthology.org/2020.acl-main.668
%U https://doi.org/10.18653/v1/2020.acl-main.668
%P 7486-7497

Markdown (Informal)

[Machine Reading of Historical Events](https://aclanthology.org/2020.acl-main.668) (Honovich et al., ACL 2020)

• Machine Reading of Historical Events (Honovich et al., ACL 2020)

ACL

• Or Honovich, Lucas Torroba Hennigen, Omri Abend, and Shay B. Cohen. 2020. Machine Reading of Historical Events. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7486–7497, Online. Association for Computational Linguistics.