Using Dependency Tree Grammar to Enhance the Reordering Model of Statistical Machine Translation Systems

Autor:	Zahra Rahimi, Shahram Khadivi, Heshaam Faili
Jazyk:	angličtina
Rok vydání:	2014
Předmět:	statistical machine translation reordering model dependency tree discriminative reordering model discriminative decoder long range reordering maximum entropy distortion model Information technology T58.5-58.64 Telecommunication TK5101-6720 Electronic computers. Computer science QA75.5-76.95
Zdroj:	International Journal of Information and Communication Technology Research, Vol 6, Iss 4, Pp 57-67 (2014)
Druh dokumentu:	article
ISSN:	2251-6107 2783-4425
Popis:	We propose three novel reordering models for statistical machine translation. These reordering models use dependency tree to improve the translation quality. All reordering models are utilized as features in a log linear framework and therefore guide the decoder to make better decisions about reordering. These reordering models are tested on two English-Persian parallel corpora with different statistics and domains. The BLEU score is improved by 2.5 on the first corpus and by 1.2 on the other.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/56056c99398f4b70abd3c43a697a8f07 Zobrazit plný text záznamu View record in DOAJ