<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bosc-vincent-2022-emergence">
    <titleInfo>
        <title>The Emergence of Argument Structure in Artificial Languages</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Tom</namePart>
        <namePart type="family">Bosc</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Pascal</namePart>
        <namePart type="family">Vincent</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>Computational approaches to the study of language emergence can help us understand how natural languages are shaped by cognitive and sociocultural factors. Previous work focused on tasks where agents refer to a single entity. In contrast, we study how agents predicate, that is, how they express that some relation holds between several entities. We introduce a setup where agents talk about a variable number of entities that can be partially observed by the listener. In the presence of a least-effort pressure, they tend to discuss only entities that are not observed by the listener. Thus we can obtain artificial phrases that denote a single entity, as well as artificial sentences that denote several entities. In natural languages, if we ignore the verb, phrases are usually concatenated, either in a specific order or by adding case markers to form sentences. Our setup allows us to quantify how much this holds in emergent languages using a metric we call concatenability. We also measure transitivity, which quantifies the importance of word order. We demonstrate the usefulness of this new setup and metrics for studying factors that influence argument structure. We compare agents having access to input representations structured into pre-segmented objects with properties, versus unstructured representations. Our results indicate that the awareness of object structure yields a more natural sentence organization.</abstract>
    <identifier type="citekey">bosc-vincent-2022-emergence</identifier>
    <identifier type="doi">10.1162/tacl_a_00524</identifier>
    <location>
        <url>https://aclanthology.org/2022.tacl-1.79</url>
    </location>
    <part>
        <date>2022</date>
        <detail type="volume"><number>10</number></detail>
        <extent unit="page">
            <start>1375</start>
            <end>1391</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T The Emergence of Argument Structure in Artificial Languages
%A Bosc, Tom
%A Vincent, Pascal
%J Transactions of the Association for Computational Linguistics
%D 2022
%V 10
%I MIT Press
%C Cambridge, MA
%F bosc-vincent-2022-emergence
%X Computational approaches to the study of language emergence can help us understand how natural languages are shaped by cognitive and sociocultural factors. Previous work focused on tasks where agents refer to a single entity. In contrast, we study how agents predicate, that is, how they express that some relation holds between several entities. We introduce a setup where agents talk about a variable number of entities that can be partially observed by the listener. In the presence of a least-effort pressure, they tend to discuss only entities that are not observed by the listener. Thus we can obtain artificial phrases that denote a single entity, as well as artificial sentences that denote several entities. In natural languages, if we ignore the verb, phrases are usually concatenated, either in a specific order or by adding case markers to form sentences. Our setup allows us to quantify how much this holds in emergent languages using a metric we call concatenability. We also measure transitivity, which quantifies the importance of word order. We demonstrate the usefulness of this new setup and metrics for studying factors that influence argument structure. We compare agents having access to input representations structured into pre-segmented objects with properties, versus unstructured representations. Our results indicate that the awareness of object structure yields a more natural sentence organization.
%R 10.1162/tacl_a_00524
%U https://aclanthology.org/2022.tacl-1.79
%U https://doi.org/10.1162/tacl_a_00524
%P 1375-1391

Markdown (Informal)

[The Emergence of Argument Structure in Artificial Languages](https://aclanthology.org/2022.tacl-1.79) (Bosc & Vincent, TACL 2022)

• The Emergence of Argument Structure in Artificial Languages (Bosc & Vincent, TACL 2022)

ACL

• Tom Bosc and Pascal Vincent. 2022. The Emergence of Argument Structure in Artificial Languages. Transactions of the Association for Computational Linguistics, 10:1375–1391.