A common and effective way to train translation systems between related languages is to consider sub-word level basic units. However, this increases the length of the sentences resulting in increased decoding time. The increase in length is also impacted by the specific choice of data format for representing the sentences as subwords. In a phrase-based SMT framework, we investigate different choices of decoder parameters as well as data format and their impact on decoding time and translation accuracy. We suggest best options for these settings that significantly improve decoding time with little impact on the translation accuracy.
There has been a lot of work looking at optimizing specific components of SMT decoders in a general setting. provide a good overview of various approaches to optimizing decoders. Some of the prominent efforts include efficient language models @cite_0 , lazy loading @cite_12 , phrase-table design @cite_7 , multi-core environment issues @cite_10 , efficient memory allocation @cite_17 , alternative stack configurations @cite_17 and alternative decoding algorithms like cube pruning @cite_16 .
@cite_7 @cite_0 @cite_16 @cite_10 @cite_12 @cite_17
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We describe the structure of a space-efficient phrase table for phrase-based statistical machine translation with the Moses decoder. The new phrase table can be used in-memory or be partially mapped on-disk. Compared to the standard Moses on-disk phrase table implementation a size reduction by a factor of 6 is achieved. Abstract In this work we introduce a new Statistical Machine Translation (SMT) system whose main objective is to reduce the translation times exploiting efficiently the computing power of the current processors and servers. Our system processes each individual job in parallel using different number of cores in such a way that the level of parallelism for each job changes dynamically according to the load of the translation server. In addition, the system is able to adapt to the particularities of any hardware platform used as server thanks to an autotuning module. An exhaustive performance evaluation considering different scenarios and hardware configurations demonstrates the benefits and flexibility of our proposal. In phrase-based statistical machine translation, the phrase-table requires a large amount of memory. We will present an efficient representation with two key properties: on-demand loading and a prefix tree structure for the source phrases. We will show that this representation scales well to large data tasks and that we are able to store hundreds of millions of phrase pairs in the phrase-table. For the large Chinese‐ English NIST task, the memory requirements of the phrase-table are reduced to less than 20MB using the new representation with no loss in translation quality and speed. Additionally, the new representation is not limited to a specific test set, which is important for online or real-time machine translation. One problem in speech translation is the matching of phrases in the input word graph and the phrase-table. We will describe a novel algorithm that effectively solves this combinatorial problem exploiting the prefix tree data structure of the phrase-table. This algorithm enables the use of significantly larger input word graphs in a more efficient way resulting in improved translation quality. The utilization of statistical machine translation (SMT) has grown enormously over the last decade, many using open-source software developed by the NLP community. As commercial use has increased, there is need for software that is optimized for commercial requirements, in particular, fast phrase-based decoding and more efficient utilization of modern multicore servers. In this paper we re-examine the major components of phrase-based decoding and decoder implementation with particular emphasis on speed and scalability on multicore machines. The result is a drop-in replacement for the Moses decoder which is up to fifteen times faster and scales monotonically with the number of cores.