We present a description of the implementation of the open source decoder for statistical machine translation which has become popular with many researchers in SMT research. The goal of the project is to create an open, high quality phrase-based decoder which can reduce the time and barrier to entry for researchers wishing to do SMT research. We discuss the major design objective for the Moses decoder, its performance relative to other SMT decoders, and the steps we are taking to ensure that its success will continue. Phrase-based translation has been one of the major advances in statistical machine translation (Brown et al. 1990) in recent years and is currently one of the techniques which can claim to be stateof-the-art in machine translation. Phrase-based models are a development of the word based models as exemplified by the (Brown et al. 1990 ). In phrase-based translation, contiguous segments of words in the input sentence are mapped to contiguous segments of words in the output sentence. In SMT, we are given a source language sentence, s, which is to be translated into a target language sentence, t. The goal of machine translation is to find the translation, t , which is defined as: ˆa rg max ( | ) t t p t s = where ( | ) p t s is the probability model. The argmax implies a search for the best translation t in the space of possible translations t. This search is the task of the decoder, which we will concentrate on in this paper. There have been numerous implementations of phrase-based decoders for SMT prior to our work. Early systems such as the Alignment Template System (ATS) (Och and Ney 2004) and Pharaoh (Koehn 2004) were widely used and accepted by the research community. ATS is perhaps the crossover system, in that word classes were translated as phrases but the surface words were translated word by word. Pharaoh substituted the word classes with surface words, thereby discarding the use of word classes in decoding altogether. There has been other phrase-based decoders such as PORTAGE (Sadat et Many early systems came with restrictive licenses; ATS has never been publicly released, Pharaoh was released in 2003 as a pre-compiled binary with documentation. This severely limited the extent to which other researchers can study and enhance the decoder. Without access to the decoder source code research was generally restricted to altering the input, augmenting it with extra information, or modifying the output or re-ranking the n-best list output. The main contribution of this paper is to show how we have created an extensible decoder, has acceptable run time performance compared to similar systems, and the ease of use and development that has made it the preferred choice for researchers looking for a phrase-based SMT decoder. 58
The open source toolkit for statistical machine translation (Moses) has been designed in REF .
Design of the Moses Decoder for Statistical Machine Translation
Software Engineering, Testing, and Quality Assurance for Natural Language Processing
Computer Science