Introduction

This file contains documentation for ISI Chinese-English Automatically Extracted Parallel Text, Linguistic Data Consortium (LDC) catalog number LDC2007T09 and isbn 1-58563-422-0.

This distribution contains a corpus of Chinese-English parallel sentences, which were extracted automatically from two monolingual corpora: Chinese Gigaword Second Edition (LDC2006T02) and English Gigaword Second Edition (LDC2005T14). The data was extracted from news articles published by Xinhua News Agency and was obtained using the automatic parallel sentence identification method described in the following publication: Dragos Stefan Munteanu, Daniel Marcu, 2005. Improving Machine Translation Performance by Exploiting Non-parallel Corpora, Computational Linguistics, 31(4):477-504

The corpus contains 558,567 sentence pairs; the word count on the English side is approximately 16M words. The sentences in the parallel corpus preserve the form and encoding of the texts in the original Gigaword corpora.

For each sentence pair in the corpus the authors provide the names of the documents from which the two sentences were extracted, as well as a confidence score (between 0.5 and 1.0), which is indicative of their degree of parallelism. The parallel sentence identification approach is designed to judge sentence pairs in isolation from their contexts, and can therefore find parallel sentences within document pairs which are not parallel. The fact that two documents share several parallel sentences does not necessarily mean the documents are parallel

In order to make this resource useful for research in Machine Translation (MT), the authors made efforts to detect potential overlaps between this data and the standard test and development data sets used by the MT community. The NIST 2002-2005 MT evaluation data sets contain several articles from Xinhua News Agency. Sentence pairs in this distribution that have a 7-gram overlap with a sentence pair in a NIST MT evaluation set or sentence pairs coming from documents whose names are similar to those in the NIST MT sets are marked with a negative confidence score.

Samples

Content Copyright

Portions © 1990-2004 Xinhua News Agency, © 2005, 2007 Trustees of the University of Pennsylvania