NIST 2012 Open Machine Translation (OpenMT) Progress Test Five Language Source

Item Name: NIST 2012 Open Machine Translation (OpenMT) Progress Test Five Language Source
Author(s): NIST Multimodal Information Group
LDC Catalog No.: LDC2014T02
ISBN: 1-58563-668-1
ISLRN: 847-333-922-514-6
Release Date: February 17, 2014
Member Year(s): 2014
DCMI Type(s): Text
Data Source(s): web collection, newswire
Project(s): NIST MT
Application(s): machine translation
Language(s): Dari, Korean, Persian, English, Mandarin Chinese, Arabic, Iranian Persian, Chinese
Language ID(s): prs, kor, fas, eng, cmn, ara, pes, zho
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2014T02 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: NIST Multimodal Information Group. NIST 2012 Open Machine Translation (OpenMT) Progress Test Five Language Source LDC2014T02. Web Download. Philadelphia: Linguistic Data Consortium, 2014.


NIST 2012 Open Machine Translation (OpenMT) Progress Test Five Language Source was developed by NIST Multimodal Information Group. This release contains the evaluation sets (source data and human reference translations), DTD, scoring software, and evaluation plan for the OpenMT 2012 test for Arabic, Chinese, Dari, Farsi, and Korean to English on a parallel data set. The set is based on a subset of the Arabic-to-English and Chinese-to-English progress tests from the OpenMT 2008, 2009 and 2012 evaluations with new source data created by humans based on the English reference translation. The package was compiled, and scoring software was developed, at NIST, making use of newswire and web data and reference translations developed by the Linguistic Data Consortium (LDC) and the Defense Language Institute Foreign Language Center.

The objective of the OpenMT evaluation series is to support research in, and help advance the state of the art of, machine translation (MT) technologies -- technologies that translate text between human languages. Input may include all forms of text. The goal is for the output to be an adequate and fluent translation of the original.

The MT evaluation series started in 2001 as part of the DARPA TIDES (Translingual Information Detection, Extraction) program. Beginning with the 2006 evaluation, the evaluations have been driven and coordinated by NIST as NIST OpenMT. These evaluations provide an important contribution to the direction of research efforts and the calibration of technical capabilities in MT. The Open MT evaluations are intended to be of interest to all researchers working on the general problem of automatic translation between human languages. To this end, they are designed to be simple, to focus on core technology issues and to be fully supported.

The 2012 task included the evaluation of five language pairs: Arabic-to-English, Chinese-to-English, Dari-to-English, Farsi-to-English and Korean-to-English in two source data styles.

For general information about the NIST OpenMT evaluations, refer to the NIST OpenMT website.

This evaluation kit includes a single Perl script ( that may be used to produce a translation quality score for one (or more) MT systems. The script works by comparing the system output translation with a set of (expert) reference translations of the same source text. Comparison is based on finding sequences of words in the reference translations that match word sequences in the system output translation.

LDC has also released the following related corpora: NIST 2012 Open Machine Translation (OpenMT) Evaluation (LDC2013T03) (material from the Chinese-to-English pair track including restricted domain data) and NIST 2008-2012 Open Machine Translation (OpenMT) Progress Test Sets (LDC2013T07) (Arabic, Chinese and English test data).


This release consists of 20 files, four for each of the five languages, presented in XML with an included DTD. The four files are source and reference data from the same source data in the following two styles:

  • English-true: an English-oriented translation this requires that the text read well and not use any idiomatic expressions in the foreign language to convey meaning, unless absolutely necessary.
  • Foreign-true: a translation as close as possible to the foreign language, as if the text had originated in that language.


Please view these samples for Arabic


None at this time.

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