Scalable Machine Translation in Memory Constrained Environments
Table 3.3: Results for parallel extraction using 8 processes/threads.
['[BOLD] Implementation', '[BOLD] Programming', '[BOLD] Time', '[BOLD] Memory']
\college St. Hugh’s College \degreeMaster by Research \degreedateTrinity 2016 {romanpages} Contents Although machine translation has been a subject of interest in the research community since the late 1940s Statistical machine translation makes use of parallel data to automatically infer rules which can later be combined to produce translations of input sentences. As with most natural language systems employing statistical models, the quality of translation systems increases as more data is used to train the underlying models. Scalability is a key concern in machine translation, as one would like to use as much data as possible when building a translation system. This chapter presents a brief introduction to statistical machine translation. Our goal is to explain how a translation system works, as we prepare the reader for the topics covered in the next chapters. We discuss the standard approaches employed by each component of a translation system and show where difficulties arise when the amount of memory is limited. We compare two formalisms that lie at the foundation of most translation systems in use today: finite state transducers and synchronous context free grammars. We focus our exposition on the topics relevant for reaching the goal of constructing a compact translation system and refer the interested reader to \newciteLopez2008b for a thorough review of statical machine translation. Online Grammar Extractors The traditional approach for storing translation models in memory is achieved with the help of phrase tables, dictionary-like data structures that map all source phrases from the training corpus to their target side correspondents. Phrase tables often become unmanageably large, especially in the case of hierarchical phrase-based systems, which make use of translation rules containing gaps. Online grammar extractors avoid loading all translation rules into memory by constructing memory efficient data structures on top of the source side of the parallel data, which are used to efficiently locate phrases in the corpus and extract translation rules on the fly during decoding. The online grammar extractor presented in this chapter extends \newciteLopez2007 and introduces a new technique for matching phrases containing gaps that significantly reduces the extraction time for hierarchical phrase-based systems.