Deyi Xiong
Min Zhang
ABSTRACT
In this paper, we propose a linguistically annotated reordering model for BTG-based statistical machine translation. The model incorporates linguistic knowledge to predict orders for both syntactic and non-syntactic phrases. The linguistic knowledge is automatically learned from source-side parse trees through an annotation algorithm. We empirically demonstrate that the proposed model leads to a significant improvement of 1.55% in the BLEU score over the baseline reordering model on the NIST MT-05 Chinese-to-English translation task.
- Franz Josef Och. 2003. Minimum Error Rate Training in Statistical Machine Translation. In Proceedings of ACL 2003. Google Scholar
Digital Library
- Andreas Stolcke. 2002. SRILM - an extensible language modeling toolkit. In Proceedings of International Conference on Spoken Language Processing, volume 2, pages 901--904.Google Scholar
- Chao Wang, Michael Collins and Philipp Koehn. 2007. Chinese Syntactic Reordering for Statistical Machine Translation. In Proceedings of EMNLP-CoNLL 2007.Google Scholar
- Dekai Wu. 1997. Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora. Computational Linguistics, 23(3):377--403. Google ScholarDigital Library
- Deyi Xiong, Shuanglong Li, Qun Liu, Shouxun Lin, Yueliang Qian. 2005. Parsing the Penn Chinese Treebank with Semantic Knowledge. In Proceedings of IJCNLP, Jeju Island, Korea. Google ScholarDigital Library
- Deyi Xiong, Qun Liu and Shouxun Lin. 2006. Maximum Entropy Based Phrase Reordering Model for Statistical Machine Translation. In Proceedings of ACL-COLING 2006. Google ScholarDigital Library
- Dongdong Zhang, Mu Li, Chi-Ho Li and Ming Zhou. 2007. Phrase Reordering Model Integrating Syntactic Knowledge for SMT. In Proceedings of EMNLP-CoNLL 2007.Google Scholar
- Le Zhang. 2004. Maximum Entropy Modeling Tooklkit for Python and C++. Available at http://homepages.inf.ed.ac.uk/s0450736/maxent_toolkit.html.Google Scholar
Index Terms
- A linguistically annotated reordering model for BTG-based statistical machine translation
Recommendations
Linguistically annotated BTG for statistical machine translation
COLING '08: Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1Bracketing Transduction Grammar (BTG) is a natural choice for effective integration of desired linguistic knowledge into statistical machine translation (SMT). In this paper, we propose a Linguistically Annotated BTG (LABTG) for SMT. It conveys ...
Sentence type based reordering model for statistical machine translation
COLING '08: Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1Many reordering approaches have been proposed for the statistical machine translation (SMT) system. However, the information about the type of source sentence is ignored in the previous works. In this paper, we propose a group of novel reordering models ...
An integrated reordering model for statistical machine translation
MICAI'07: Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligenceIn this paper, we propose a phrase reordering model for statistical machine translation. The model is derived from the bracketing ITG, and integrates the local and global reordering model. We present a method to extract phrase pairs from a word-aligned ...
Comments