<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="akiba-etal-2001-using">
    <titleInfo>
        <title>Using multiple edit distances to automatically rank machine translation output</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yasuhiro</namePart>
        <namePart type="family">Akiba</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kenji</namePart>
        <namePart type="family">Imamura</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Eiichiro</namePart>
        <namePart type="family">Sumita</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2001-sep 18-22</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of Machine Translation Summit VIII</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Bente</namePart>
            <namePart type="family">Maegaard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <place>
                <placeTerm type="text">Santiago de Compostela, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper addresses the challenging problem of automatically evaluating output from machine translation (MT) systems in order to support the developers of these systems. Conventional approaches to the problem include methods that automatically assign a rank such as A, B, C, or D to MT output according to a single edit distance between this output and a correct translation example. The single edit distance can be differently designed, but changing its design makes assigning a certain rank more accurate, but another rank less accurate. This inhibits improving accuracy of rank assignment. To overcome this obstacle, this paper proposes an automatic ranking method that, by using multiple edit distances, encodes machine-translated sentences with a rank assigned by humans into multi-dimensional vectors from which a classifier of ranks is learned in the form of a decision tree (DT). The proposed method assigns a rank to MT output through the learned DT. The proposed method is evaluated using transcribed texts of real conversations in the travel arrangement domain. Experimental results show that the proposed method is more accurate than the single-edit-distance-based ranking methods, in both closed and open tests. Moreover, the proposed method could estimate MT quality within 3% error in some cases.</abstract>
    <identifier type="citekey">akiba-etal-2001-using</identifier>
    <location>
        <url>https://aclanthology.org/2001.mtsummit-papers.3</url>
    </location>
    <part>
        <date>2001-sep 18-22</date>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Using multiple edit distances to automatically rank machine translation output
%A Akiba, Yasuhiro
%A Imamura, Kenji
%A Sumita, Eiichiro
%Y Maegaard, Bente
%S Proceedings of Machine Translation Summit VIII
%D 2001
%8 sep 18 22
%C Santiago de Compostela, Spain
%F akiba-etal-2001-using
%X This paper addresses the challenging problem of automatically evaluating output from machine translation (MT) systems in order to support the developers of these systems. Conventional approaches to the problem include methods that automatically assign a rank such as A, B, C, or D to MT output according to a single edit distance between this output and a correct translation example. The single edit distance can be differently designed, but changing its design makes assigning a certain rank more accurate, but another rank less accurate. This inhibits improving accuracy of rank assignment. To overcome this obstacle, this paper proposes an automatic ranking method that, by using multiple edit distances, encodes machine-translated sentences with a rank assigned by humans into multi-dimensional vectors from which a classifier of ranks is learned in the form of a decision tree (DT). The proposed method assigns a rank to MT output through the learned DT. The proposed method is evaluated using transcribed texts of real conversations in the travel arrangement domain. Experimental results show that the proposed method is more accurate than the single-edit-distance-based ranking methods, in both closed and open tests. Moreover, the proposed method could estimate MT quality within 3% error in some cases.
%U https://aclanthology.org/2001.mtsummit-papers.3

Markdown (Informal)

[Using multiple edit distances to automatically rank machine translation output](https://aclanthology.org/2001.mtsummit-papers.3) (Akiba et al., MTSummit 2001)

• Using multiple edit distances to automatically rank machine translation output (Akiba et al., MTSummit 2001)

ACL

• Yasuhiro Akiba, Kenji Imamura, and Eiichiro Sumita. 2001. Using multiple edit distances to automatically rank machine translation output. In Proceedings of Machine Translation Summit VIII, Santiago de Compostela, Spain.