<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ataman-etal-2018-compositional">
    <titleInfo>
        <title>Compositional Source Word Representations for Neural Machine Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Duygu</namePart>
        <namePart type="family">Ataman</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mattia</namePart>
        <namePart type="given">Antonino</namePart>
        <namePart type="family">Di Gangi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marcello</namePart>
        <namePart type="family">Federico</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 21st Annual Conference of the European Association for Machine Translation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Pérez-Ortiz</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Felipe</namePart>
            <namePart type="family">Sánchez-Martínez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Miquel</namePart>
            <namePart type="family">Esplà-Gomis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Maja</namePart>
            <namePart type="family">Popović</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Celia</namePart>
            <namePart type="family">Rico</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">André</namePart>
            <namePart type="family">Martins</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joachim</namePart>
            <namePart type="family">Van den Bogaert</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mikel</namePart>
            <namePart type="given">L</namePart>
            <namePart type="family">Forcada</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <place>
                <placeTerm type="text">Alicante, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The requirement for neural machine translation (NMT) models to use fixed-size input and output vocabularies plays an important role for their accuracy and generalization capability. The conventional approach to cope with this limitation is performing translation based on a vocabulary of sub-word units that are predicted using statistical word segmentation methods. However, these methods have recently shown to be prone to morphological errors, which lead to inaccurate translations. In this paper, we extend the source-language embedding layer of the NMT model with a bi-directional recurrent neural network that generates compositional representations of the source words from embeddings of character n-grams. Our model consistently outperforms conventional NMT with sub-word units on four translation directions with varying degrees of morphological complexity and data sparseness on the source side.</abstract>
    <identifier type="citekey">ataman-etal-2018-compositional</identifier>
    <location>
        <url>https://aclanthology.org/2018.eamt-main.3</url>
    </location>
    <part>
        <date>2018-05</date>
        <extent unit="page">
            <start>51</start>
            <end>60</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Compositional Source Word Representations for Neural Machine Translation
%A Ataman, Duygu
%A Di Gangi, Mattia Antonino
%A Federico, Marcello
%Y Pérez-Ortiz, Juan Antonio
%Y Sánchez-Martínez, Felipe
%Y Esplà-Gomis, Miquel
%Y Popović, Maja
%Y Rico, Celia
%Y Martins, André
%Y Van den Bogaert, Joachim
%Y Forcada, Mikel L.
%S Proceedings of the 21st Annual Conference of the European Association for Machine Translation
%D 2018
%8 May
%C Alicante, Spain
%F ataman-etal-2018-compositional
%X The requirement for neural machine translation (NMT) models to use fixed-size input and output vocabularies plays an important role for their accuracy and generalization capability. The conventional approach to cope with this limitation is performing translation based on a vocabulary of sub-word units that are predicted using statistical word segmentation methods. However, these methods have recently shown to be prone to morphological errors, which lead to inaccurate translations. In this paper, we extend the source-language embedding layer of the NMT model with a bi-directional recurrent neural network that generates compositional representations of the source words from embeddings of character n-grams. Our model consistently outperforms conventional NMT with sub-word units on four translation directions with varying degrees of morphological complexity and data sparseness on the source side.
%U https://aclanthology.org/2018.eamt-main.3
%P 51-60

Markdown (Informal)

[Compositional Source Word Representations for Neural Machine Translation](https://aclanthology.org/2018.eamt-main.3) (Ataman et al., EAMT 2018)

• Compositional Source Word Representations for Neural Machine Translation (Ataman et al., EAMT 2018)

ACL

• Duygu Ataman, Mattia Antonino Di Gangi, and Marcello Federico. 2018. Compositional Source Word Representations for Neural Machine Translation. In Proceedings of the 21st Annual Conference of the European Association for Machine Translation, pages 51–60, Alicante, Spain.