Abstract
Parallel text is one of the most valuable resources for development of statistical machine translation systems and other NLP applications. The Linguistic Data Consortium (LDC) has supported research on statistical machine translations and other NLP applications by creating and distributing a large amount of parallel text resources for the research communities. However, manual translations are very costly, and the number of known providers that offer complete parallel text is limited. This paper presents a cost effective approach to identify parallel document pairs from sources that provide potential parallel text - namely, sources that may contain whole or partial translations of documents in the source language - using the BITS and Champollion parallel text alignment systems developed by LDC.- Anthology ID:
- L08-1582
- Volume:
- Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)
- Month:
- May
- Year:
- 2008
- Address:
- Marrakech, Morocco
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2008/pdf/779_paper.pdf
- DOI:
- Cite (ACL):
- Kazuaki Maeda, Xiaoyi Ma, and Stephanie Strassel. 2008. Creating Sentence-Aligned Parallel Text Corpora from a Large Archive of Potential Parallel Text using BITS and Champollion. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).
- Cite (Informal):
- Creating Sentence-Aligned Parallel Text Corpora from a Large Archive of Potential Parallel Text using BITS and Champollion (Maeda et al., LREC 2008)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2008/pdf/779_paper.pdf