<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="arroyo-fernandez-meza-ruiz-2017-lipn">
    <titleInfo>
        <title>LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ignacio</namePart>
        <namePart type="family">Arroyo-Fernández</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ivan</namePart>
        <namePart type="given">Vladimir</namePart>
        <namePart type="family">Meza Ruiz</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-aug</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vancouver, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper we report our attempt to use, on the one hand, state-of-the-art neural approaches that are proposed to measure Semantic Textual Similarity (STS). On the other hand, we propose an unsupervised cross-word alignment approach, which is linguistically motivated. The neural approaches proposed herein are divided into two main stages. The first stage deals with constructing neural word embeddings, the components of sentence embeddings. The second stage deals with constructing a semantic similarity function relating pairs of sentence embeddings. Unfortunately our competition results were poor in all tracks, therefore we concentrated our research to improve them for Track 5 (EN-EN).</abstract>
    <identifier type="citekey">arroyo-fernandez-meza-ruiz-2017-lipn</identifier>
    <identifier type="doi">10.18653/v1/S17-2031</identifier>
    <location>
        <url>https://aclanthology.org/S17-2031</url>
    </location>
    <part>
        <date>2017-aug</date>
        <extent unit="page">
            <start>208</start>
            <end>212</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity
%A Arroyo-Fernández, Ignacio
%A Meza Ruiz, Ivan Vladimir
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 aug
%I Association for Computational Linguistics
%C Vancouver, Canada
%F arroyo-fernandez-meza-ruiz-2017-lipn
%X In this paper we report our attempt to use, on the one hand, state-of-the-art neural approaches that are proposed to measure Semantic Textual Similarity (STS). On the other hand, we propose an unsupervised cross-word alignment approach, which is linguistically motivated. The neural approaches proposed herein are divided into two main stages. The first stage deals with constructing neural word embeddings, the components of sentence embeddings. The second stage deals with constructing a semantic similarity function relating pairs of sentence embeddings. Unfortunately our competition results were poor in all tracks, therefore we concentrated our research to improve them for Track 5 (EN-EN).
%R 10.18653/v1/S17-2031
%U https://aclanthology.org/S17-2031
%U https://doi.org/10.18653/v1/S17-2031
%P 208-212

Markdown (Informal)

[LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity](https://aclanthology.org/S17-2031) (Arroyo-Fernández & Meza Ruiz, SemEval 2017)

• LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity (Arroyo-Fernández & Meza Ruiz, SemEval 2017)

ACL

• Ignacio Arroyo-Fernández and Ivan Vladimir Meza Ruiz. 2017. LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 208–212, Vancouver, Canada. Association for Computational Linguistics.