<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-moldovan-2018-rule">
    <titleInfo>
        <title>Rule-based vs. Neural Net Approaches to Semantic Textual Similarity</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Linrui</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dan</namePart>
        <namePart type="family">Moldovan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Peter</namePart>
            <namePart type="family">Machonis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anabela</namePart>
            <namePart type="family">Barreiro</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kristina</namePart>
            <namePart type="family">Kocijan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Max</namePart>
            <namePart type="family">Silberztein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Santa Fe, New Mexico, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper presents a neural net approach to determine Semantic Textual Similarity (STS) using attention-based bidirectional Long Short-Term Memory Networks (Bi-LSTM). To this date, most of the traditional STS systems were rule-based that built on top of excessive use of linguistic features and resources. In this paper, we present an end-to-end attention-based Bi-LSTM neural network system that solely takes word-level features, without expensive feature engineering work or the usage of external resources. By comparing its performance with traditional rule-based systems against SemEval-2012 benchmark, we make an assessment on the limitations and strengths of neural net systems to rule-based systems on Semantic Textual Similarity.</abstract>
    <identifier type="citekey">zhang-moldovan-2018-rule</identifier>
    <location>
        <url>https://aclanthology.org/W18-3803/</url>
    </location>
    <part>
        <date>2018-08</date>
        <extent unit="page">
            <start>12</start>
            <end>17</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Rule-based vs. Neural Net Approaches to Semantic Textual Similarity
%A Zhang, Linrui
%A Moldovan, Dan
%Y Machonis, Peter
%Y Barreiro, Anabela
%Y Kocijan, Kristina
%Y Silberztein, Max
%S Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F zhang-moldovan-2018-rule
%X This paper presents a neural net approach to determine Semantic Textual Similarity (STS) using attention-based bidirectional Long Short-Term Memory Networks (Bi-LSTM). To this date, most of the traditional STS systems were rule-based that built on top of excessive use of linguistic features and resources. In this paper, we present an end-to-end attention-based Bi-LSTM neural network system that solely takes word-level features, without expensive feature engineering work or the usage of external resources. By comparing its performance with traditional rule-based systems against SemEval-2012 benchmark, we make an assessment on the limitations and strengths of neural net systems to rule-based systems on Semantic Textual Similarity.
%U https://aclanthology.org/W18-3803/
%P 12-17

Markdown (Informal)

[Rule-based vs. Neural Net Approaches to Semantic Textual Similarity](https://aclanthology.org/W18-3803/) (Zhang & Moldovan, LR4NLP 2018)

• Rule-based vs. Neural Net Approaches to Semantic Textual Similarity (Zhang & Moldovan, LR4NLP 2018)

ACL

• Linrui Zhang and Dan Moldovan. 2018. Rule-based vs. Neural Net Approaches to Semantic Textual Similarity. In Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing, pages 12–17, Santa Fe, New Mexico, USA. Association for Computational Linguistics.