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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F11%3A10107805%21RIV12-GA0-11320___
rdf:type
n9:Vysledek skos:Concept
dcterms:description
This paper examines two techniques of manual evaluation that can be used to identify error types of individual machine translation systems. The first technique of %22blind post-editing%22 is being used in WMT evaluation campaigns since 2009 and manually constructed data of this type are available for various language pairs. The second technique of explicit marking of errors has been used in the past as well. We propose a method for interpreting blind post-editing data at a finer level and compare the results with explicit marking of errors. While the human annotation of either of the techniques is not exactly reproducible (relatively low agreement), both techniques lead to similar observations of differences of the systems. Specifically, we are able to suggest which errors in MT output are easy and hard to correct with no access to the source, a situation experienced by users who do not understand the source language. This paper examines two techniques of manual evaluation that can be used to identify error types of individual machine translation systems. The first technique of %22blind post-editing%22 is being used in WMT evaluation campaigns since 2009 and manually constructed data of this type are available for various language pairs. The second technique of explicit marking of errors has been used in the past as well. We propose a method for interpreting blind post-editing data at a finer level and compare the results with explicit marking of errors. While the human annotation of either of the techniques is not exactly reproducible (relatively low agreement), both techniques lead to similar observations of differences of the systems. Specifically, we are able to suggest which errors in MT output are easy and hard to correct with no access to the source, a situation experienced by users who do not understand the source language.
dcterms:title
Analyzing Error Types in English-Czech Machine Translation Analyzing Error Types in English-Czech Machine Translation
skos:prefLabel
Analyzing Error Types in English-Czech Machine Translation Analyzing Error Types in English-Czech Machine Translation
skos:notation
RIV/00216208:11320/11:10107805!RIV12-GA0-11320___
n9:predkladatel
n12:orjk%3A11320
n6:aktivita
n17:P
n6:aktivity
P(7E09003), P(GAP406/11/1499), P(GPP406/10/P259)
n6:cisloPeriodika
1
n6:dodaniDat
n15:2012
n6:domaciTvurceVysledku
n19:2630176
n6:druhVysledku
n18:J
n6:duvernostUdaju
n20:S
n6:entitaPredkladatele
n10:predkladatel
n6:idSjednocenehoVysledku
186206
n6:idVysledku
RIV/00216208:11320/11:10107805
n6:jazykVysledku
n11:eng
n6:klicovaSlova
translation; machine; czech; english; types; error; analyzing
n6:klicoveSlovo
n7:translation n7:types n7:machine n7:error n7:czech n7:analyzing n7:english
n6:kodStatuVydavatele
CZ - Česká republika
n6:kontrolniKodProRIV
[E883914E9F9D]
n6:nazevZdroje
The Prague Bulletin of Mathematical Linguistics
n6:obor
n13:AI
n6:pocetDomacichTvurcuVysledku
1
n6:pocetTvurcuVysledku
1
n6:projekt
n16:GAP406%2F11%2F1499 n16:GPP406%2F10%2FP259 n16:7E09003
n6:rokUplatneniVysledku
n15:2011
n6:svazekPeriodika
95
n6:tvurceVysledku
Bojar, Ondřej
s:issn
0032-6585
s:numberOfPages
14
n8:doi
10.2478/v10108-011-0005-2
n3:organizacniJednotka
11320