An Overview of the SEBAMAT Project

Reinhard Rapp; George Tambouratzis

An Overview of the SEBAMAT Project

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use <b>...</b> for bold, <i>...</i> for italic, and <url>...</url> for URLs.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

SEBAMAT (semantics-based MT) is a Marie Curie project intended to con-tribute to the state of the art in machine translation (MT). Current MT systems typically take the semantics of a text only in so far into account as they are implicit in the underlying text corpora or dictionaries. Occasionally it has been argued that it may be difficult to advance MT quality to the next level as long as the systems do not make more explicit use of semantic knowledge. SEBAMAT aims to evaluate three approaches incorporating such knowledge into MT.

Anthology ID:: 2020.eamt-1.66
Volume:: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
Month:: November
Year:: 2020
Address:: Lisboa, Portugal
Editors:: André Martins, Helena Moniz, Sara Fumega, Bruno Martins, Fernando Batista, Luisa Coheur, Carla Parra, Isabel Trancoso, Marco Turchi, Arianna Bisazza, Joss Moorkens, Ana Guerberof, Mary Nurminen, Lena Marg, Mikel L. Forcada
Venue:: EAMT
SIG:
Publisher:: European Association for Machine Translation
Note:
Pages:: 491–492
Language:
URL:: https://aclanthology.org/2020.eamt-1.66/
DOI:
Bibkey:
Cite (ACL):: Reinhard Rapp and George Tambouratzis. 2020. An Overview of the SEBAMAT Project. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 491–492, Lisboa, Portugal. European Association for Machine Translation.
Cite (Informal):: An Overview of the SEBAMAT Project (Rapp & Tambouratzis, EAMT 2020)
Copy Citation:
PDF:: https://aclanthology.org/2020.eamt-1.66.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{rapp-tambouratzis-2020-overview,
    title = "An Overview of the {SEBAMAT} Project",
    author = "Rapp, Reinhard  and
      Tambouratzis, George",
    editor = "Martins, Andr{\'e}  and
      Moniz, Helena  and
      Fumega, Sara  and
      Martins, Bruno  and
      Batista, Fernando  and
      Coheur, Luisa  and
      Parra, Carla  and
      Trancoso, Isabel  and
      Turchi, Marco  and
      Bisazza, Arianna  and
      Moorkens, Joss  and
      Guerberof, Ana  and
      Nurminen, Mary  and
      Marg, Lena  and
      Forcada, Mikel L.",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2020.eamt-1.66/",
    pages = "491--492",
    abstract = "SEBAMAT (semantics-based MT) is a Marie Curie project intended to con-tribute to the state of the art in machine translation (MT). Current MT systems typically take the semantics of a text only in so far into account as they are implicit in the underlying text corpora or dictionaries. Occasionally it has been argued that it may be difficult to advance MT quality to the next level as long as the systems do not make more explicit use of semantic knowledge. SEBAMAT aims to evaluate three approaches incorporating such knowledge into MT."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="rapp-tambouratzis-2020-overview">
    <titleInfo>
        <title>An Overview of the SEBAMAT Project</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Reinhard</namePart>
        <namePart type="family">Rapp</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">George</namePart>
        <namePart type="family">Tambouratzis</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 22nd Annual Conference of the European Association for Machine Translation</title>
        </titleInfo>
        <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">Helena</namePart>
            <namePart type="family">Moniz</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sara</namePart>
            <namePart type="family">Fumega</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Bruno</namePart>
            <namePart type="family">Martins</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Batista</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Luisa</namePart>
            <namePart type="family">Coheur</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Carla</namePart>
            <namePart type="family">Parra</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Isabel</namePart>
            <namePart type="family">Trancoso</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marco</namePart>
            <namePart type="family">Turchi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Arianna</namePart>
            <namePart type="family">Bisazza</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joss</namePart>
            <namePart type="family">Moorkens</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ana</namePart>
            <namePart type="family">Guerberof</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mary</namePart>
            <namePart type="family">Nurminen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lena</namePart>
            <namePart type="family">Marg</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>
            <publisher>European Association for Machine Translation</publisher>
            <place>
                <placeTerm type="text">Lisboa, Portugal</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>SEBAMAT (semantics-based MT) is a Marie Curie project intended to con-tribute to the state of the art in machine translation (MT). Current MT systems typically take the semantics of a text only in so far into account as they are implicit in the underlying text corpora or dictionaries. Occasionally it has been argued that it may be difficult to advance MT quality to the next level as long as the systems do not make more explicit use of semantic knowledge. SEBAMAT aims to evaluate three approaches incorporating such knowledge into MT.</abstract>
    <identifier type="citekey">rapp-tambouratzis-2020-overview</identifier>
    <location>
        <url>https://aclanthology.org/2020.eamt-1.66/</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>491</start>
            <end>492</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T An Overview of the SEBAMAT Project
%A Rapp, Reinhard
%A Tambouratzis, George
%Y Martins, André
%Y Moniz, Helena
%Y Fumega, Sara
%Y Martins, Bruno
%Y Batista, Fernando
%Y Coheur, Luisa
%Y Parra, Carla
%Y Trancoso, Isabel
%Y Turchi, Marco
%Y Bisazza, Arianna
%Y Moorkens, Joss
%Y Guerberof, Ana
%Y Nurminen, Mary
%Y Marg, Lena
%Y Forcada, Mikel L.
%S Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
%D 2020
%8 November
%I European Association for Machine Translation
%C Lisboa, Portugal
%F rapp-tambouratzis-2020-overview
%X SEBAMAT (semantics-based MT) is a Marie Curie project intended to con-tribute to the state of the art in machine translation (MT). Current MT systems typically take the semantics of a text only in so far into account as they are implicit in the underlying text corpora or dictionaries. Occasionally it has been argued that it may be difficult to advance MT quality to the next level as long as the systems do not make more explicit use of semantic knowledge. SEBAMAT aims to evaluate three approaches incorporating such knowledge into MT.
%U https://aclanthology.org/2020.eamt-1.66/
%P 491-492

Download as File

Markdown (Informal)

[An Overview of the SEBAMAT Project](https://aclanthology.org/2020.eamt-1.66/) (Rapp & Tambouratzis, EAMT 2020)

An Overview of the SEBAMAT Project (Rapp & Tambouratzis, EAMT 2020)

ACL

Reinhard Rapp and George Tambouratzis. 2020. An Overview of the SEBAMAT Project. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 491–492, Lisboa, Portugal. European Association for Machine Translation.