OLAC Record oai:www.ldc.upenn.edu:LDC2010T14

Metadata
Title:		NIST 2005 Open Machine Translation (OpenMT) Evaluation
Access Rights:		Licensing Instructions for Subscription & Standard Members, and Non-Members: http://www.ldc.upenn.edu/language-resources/data/obtaining
Bibliographic Citation:		NIST Multimodal Information Group. NIST 2005 Open Machine Translation (OpenMT) Evaluation LDC2010T14. Web Download. Philadelphia: Linguistic Data Consortium, 2010
Contributor:		NIST Multimodal Information Group
Date (W3CDTF):		2010
Date Issued (W3CDTF):		2010-08-18
Description:		*Introduction* NIST 2005 Open Machine Translation (OpenMT) Evaluation, Linguistic Data Consortium (LDC) catalog number LDC2010T14 and isbn 1-58563-556-1, is a package containing source data, reference translations, and scoring software used in the NIST 2005 OpenMT evaluation. It is designed to help evaluate the effectiveness of machine translation systems. The package was compiled and scoring software was developed by researchers at NIST, making use of newswire source data and reference translations collected and developed by LDC. The objective of the NIST 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 OpenMT 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 2005 task was to evaluate translation from Chinese to English and from Arabic to English. Additional information about these evaluations may be found at the NIST Open Machine Translation (OpenMT) Evaluation web site. *Scoring Tools* This evaluation kit includes a single Perl script (mteval-v11b.pl) 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. More information on the evaluation algorithm may be obtained from the paper detailing the algorithm: BLEU: a Method for Automatic Evaluation of Machine Translation (Papineni et al, 2002). The included scoring script was released with the original evaluation, intended for use with SGML-formatted data files, and is provided to ensure compatibility of user scoring results with results from the original evaluation. An updated scoring software package (mteval-v13a-20091001.tar.gz), with XML support, additional options and bug fixes, documentation, and example translations, may be downloaded from the NIST Multimodal Information Group Tools website. *Data* This corpus consists of 100 Arabic newswire documents, 100 Chinese newswire documents, and a corresponding set of four separate human expert reference translations. Source text for both languages was collected from Agence France-Presse and Xinhua News Agency in December 2004 and January 2005. The reference translations included in this corpus have not previously been publicly available. Arabic source text from December 2004 has been available in LDC's Arabic Gigaword releases beginning with the Second Edition (LDC2006T02), and from January 2005 beginning with the Third Edition (LDC2007T40). Chinese source text from Xinhua December 2004 has been available in LDC's Chinese Gigaword releases beginning with the Second Edition (LDC2005T14), and from Xinhua January 2005 and AFP beginning with the Third Edition (LDC2007T38). For each language, the test set consists of two files: a source and a reference file. Each reference file contains four independent translations of the data set. The evaluation year, source language, test set (which, by default, is "evalset"), version of the data, and source vs. reference file (with the latter being indicated by "-ref") are reflected in the file name. DARPA TIDES MT and NIST OpenMT evaluations used SGML-formatted test data until 2008 and XML-formatted test data thereafter. The files in this package are provided in both formats. *Sample* Sample text file containing excerpts from different xml files included in this corpus, including reference translations and source text for a single newswire document. The file is encoded in UTF-8. *Updates* No updates are available at this time.
Extent:		Corpus size: 4947 KB
Identifier:		LDC2010T14
		https://catalog.ldc.upenn.edu/LDC2010T14
		ISBN: 1-58563-556-1
		ISLRN: 048-978-532-143-4
		DOI: 10.35111/7gan-5j45
Language:		Mandarin Chinese
		Standard Arabic
		Arabic
Language (ISO639):		cmn
		arb
		ara
License:		LDC User Agreement for Non-Members: https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf
Medium:		Distribution: Web Download
Publisher:		Linguistic Data Consortium
Publisher (URI):		https://www.ldc.upenn.edu
Relation (URI):		https://catalog.ldc.upenn.edu/docs/LDC2010T14
Rights Holder:		Portions © 2004-2005 Agence France Presse, © 2004-2005 Xinhua News Agency, © 2005, 2006, 2007, 2010 Trustees of the University of Pennsylvania.
Type (DCMI):		Text
Type (OLAC):		primary_text
OLAC Info
Archive:		The LDC Corpus Catalog
Description:		http://www.language-archives.org/archive/www.ldc.upenn.edu
GetRecord:		OAI-PMH request for OLAC format
GetRecord:		Pre-generated XML file
OAI Info
OaiIdentifier:		oai:www.ldc.upenn.edu:LDC2010T14
DateStamp:		2020-11-30
GetRecord:		OAI-PMH request for simple DC format
Search Info
Citation:		NIST Multimodal Information Group. 2010. Linguistic Data Consortium.
Terms:		area_Asia country_CN country_SA dcmi_Text iso639_ara iso639_arb iso639_cmn olac_primary_text