N-Gram-Based Statistical Machine Translation versus Syntax Augmented Machine Translation: Comparison and System Combination

In this paper we compare and contrast two approaches to Machine Translation (MT): the CMU-UKA Syntax Augmented Machine Translation system (SAMT) and UPC-TALP N-gram-based Statistical Machine Translation (SMT). SAMT is a hierarchical syntax-driven translation system underlain by a phrase-based model and a target part parse tree. In N-gram-based SMT, the translation process is based on bilingual units related to word-to-word alignment and statistical modeling of the bilingual context following a maximumentropy framework. We provide a stepby-step comparison of the systems and report results in terms of automatic evaluation metrics and required computational resources for a smaller Arabic-to-English

• Type: Conference
• Year: 2009
• Where: EACL
• Authors: José A. R. Fonollosa, Maxim Khalilov
• Keywords: EACL 2009, Hierarchical Syntax-driven Translation, Natural Language Processing, Statistical Machine Translation, Syntax Augmented Machine