<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="opitz-etal-2023-amr4nli">
    <titleInfo>
        <title>AMR4NLI: Interpretable and robust NLI measures from semantic graphs</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Juri</namePart>
        <namePart type="family">Opitz</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shira</namePart>
        <namePart type="family">Wein</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Julius</namePart>
        <namePart type="family">Steen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anette</namePart>
        <namePart type="family">Frank</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nathan</namePart>
        <namePart type="family">Schneider</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 15th International Conference on Computational Semantics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Maxime</namePart>
            <namePart type="family">Amblard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Breitholtz</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Nancy, France</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis. NLI benchmarks contain human ratings of entailment, but the meaning relationships driving these ratings are not formalized. Can the underlying sentence pair relationships be made more explicit in an interpretable yet robust fashion? We compare semantic structures to represent premise and hypothesis, including sets of *contextualized embeddings* and *semantic graphs* (Abstract Meaning Representations), and measure whether the hypothesis is a semantic substructure of the premise, utilizing interpretable metrics. Our evaluation on three English benchmarks finds value in both contextualized embeddings and semantic graphs; moreover, they provide complementary signals, and can be leveraged together in a hybrid model.</abstract>
    <identifier type="citekey">opitz-etal-2023-amr4nli</identifier>
    <location>
        <url>https://aclanthology.org/2023.iwcs-1.29</url>
    </location>
    <part>
        <date>2023-06</date>
        <extent unit="page">
            <start>275</start>
            <end>283</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T AMR4NLI: Interpretable and robust NLI measures from semantic graphs
%A Opitz, Juri
%A Wein, Shira
%A Steen, Julius
%A Frank, Anette
%A Schneider, Nathan
%Y Amblard, Maxime
%Y Breitholtz, Ellen
%S Proceedings of the 15th International Conference on Computational Semantics
%D 2023
%8 June
%I Association for Computational Linguistics
%C Nancy, France
%F opitz-etal-2023-amr4nli
%X The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis. NLI benchmarks contain human ratings of entailment, but the meaning relationships driving these ratings are not formalized. Can the underlying sentence pair relationships be made more explicit in an interpretable yet robust fashion? We compare semantic structures to represent premise and hypothesis, including sets of *contextualized embeddings* and *semantic graphs* (Abstract Meaning Representations), and measure whether the hypothesis is a semantic substructure of the premise, utilizing interpretable metrics. Our evaluation on three English benchmarks finds value in both contextualized embeddings and semantic graphs; moreover, they provide complementary signals, and can be leveraged together in a hybrid model.
%U https://aclanthology.org/2023.iwcs-1.29
%P 275-283

Markdown (Informal)

[AMR4NLI: Interpretable and robust NLI measures from semantic graphs](https://aclanthology.org/2023.iwcs-1.29) (Opitz et al., IWCS 2023)

• AMR4NLI: Interpretable and robust NLI measures from semantic graphs (Opitz et al., IWCS 2023)

ACL

• Juri Opitz, Shira Wein, Julius Steen, Anette Frank, and Nathan Schneider. 2023. AMR4NLI: Interpretable and robust NLI measures from semantic graphs. In Proceedings of the 15th International Conference on Computational Semantics, pages 275–283, Nancy, France. Association for Computational Linguistics.