Summary of the paper
| Title | UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation |
|---|---|
| Authors | Liang Tian, Derek F. Wong, Lidia S. Chao, Paulo Quaresma, Francisco Oliveira and Lu Yi |
| Abstract | Parallel corpus is a valuable resource for cross-language information retrieval and data-driven natural language processing systems, especially for Statistical Machine Translation (SMT). However, most existing parallel corpora to Chinese are subject to in-house use, while others are domain specific and limited in size. To a certain degree, this limits the SMT research. This paper describes the acquisition of a large scale and high quality parallel corpora for English and Chinese. The corpora constructed in this paper contain about 15 million English-Chinese (E-C) parallel sentences, and more than 2 million training data and 5,000 testing sentences are made publicly available. Different from previous work, the corpus is designed to embrace eight different domains. Some of them are further categorized into different topics. The corpus will be released to the research community, which is available at the NLP2CT website. |
| Topics | Authoring Tools, Digital Libraries |
| Full paper |
UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation |
| Bibtex | @InProceedings{TIAN14.774, author = {Liang Tian and Derek F. Wong and Lidia S. Chao and Paulo Quaresma and Francisco Oliveira and Lu Yi}, title = {UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |