We describe a multi-step process for automatically learning reliable sub-sentential syntactic phrases that are translation equivalents of each other and syntactic translation rules between two languages. The input to the process is a corpus of parallel sentences, word-aligned and annotated with phrase-structure parse trees. We first apply a newly developed algorithm for aligning parse-tree nodes between the two parallel trees. Next, we extract all aligned sub-sentential syntactic constituents from the parallel sentences, and create a syntax-based phrase-table. Finally, we treat the node alignments as tree decomposition points and extract from the corpus all possible synchronous parallel tree fragments. These are then converted into synchronous context-free rules. We describe the approach and analyze its application to Chinese-English parallel data. 1

This paper describes ATR’s hybrid approach to spoken language translation and it’s application to the IWSLT 2005 translation task. Multiple corpus-based translation engines are used to translate the same input, whereby the best translation among the element MT outputs is selected according to statistical models. The evaluation results of the Japanese-to-English and Chinese-to-English translation tasks for different training data conditions showed the potential of the proposed hybrid approach and revealed new directions in how to improve the current system performance. 1.

Abstract. The CMU Statistical Transfer Framework (Stat-XFER) is a general framework for developing search-based syntax-driven machine translation (MT) systems. The framework consists of an underlying syntaxbased transfer formalism along with a collection of software components designed to facilitate the development of a broad range of MT research systems. The main components are a general language-independent runtime transfer engine and decoder, along with several different tools for creating the various underlying language-pair-specific resources that are required for building a specific MT system for any given language pair. We describe the general framework, its unique properties and features, and its application to the construction of MT research prototype systems for a diverse collection of language pairs. 1