<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="caselli-etal-2022-time">
    <titleInfo>
        <title>How about Time? Probing a Multilingual Language Model for Temporal Relations</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Tommaso</namePart>
        <namePart type="family">Caselli</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Irene</namePart>
        <namePart type="family">Dini</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Felice</namePart>
        <namePart type="family">Dell’Orletta</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 29th International Conference on Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <publisher>International Committee on Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Gyeongju, Republic of Korea</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper presents a comprehensive set of probing experiments using a multilingual language model, XLM-R, for temporal relation classification between events in four languages. Results show an advantage of contextualized embeddings over static ones and a detrimen- tal role of sentence level embeddings. While obtaining competitive results against state-of-the-art systems, our probes indicate a lack of suitable encoded information to properly address this task.</abstract>
    <identifier type="citekey">caselli-etal-2022-time</identifier>
    <location>
        <url>https://aclanthology.org/2022.coling-1.283</url>
    </location>
    <part>
        <date>2022-10</date>
        <extent unit="page">
            <start>3197</start>
            <end>3209</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T How about Time? Probing a Multilingual Language Model for Temporal Relations
%A Caselli, Tommaso
%A Dini, Irene
%A Dell’Orletta, Felice
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F caselli-etal-2022-time
%X This paper presents a comprehensive set of probing experiments using a multilingual language model, XLM-R, for temporal relation classification between events in four languages. Results show an advantage of contextualized embeddings over static ones and a detrimen- tal role of sentence level embeddings. While obtaining competitive results against state-of-the-art systems, our probes indicate a lack of suitable encoded information to properly address this task.
%U https://aclanthology.org/2022.coling-1.283
%P 3197-3209

Markdown (Informal)

[How about Time? Probing a Multilingual Language Model for Temporal Relations](https://aclanthology.org/2022.coling-1.283) (Caselli et al., COLING 2022)

• How about Time? Probing a Multilingual Language Model for Temporal Relations (Caselli et al., COLING 2022)

ACL

• Tommaso Caselli, Irene Dini, and Felice Dell’Orletta. 2022. How about Time? Probing a Multilingual Language Model for Temporal Relations. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3197–3209, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.