Faster Decoding for Subword Level Phrase-based SMT between Related Languages
نویسندگان
چکیده
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.
منابع مشابه
Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT
We investigate pivot-based translation between related languages in a low resource, phrase-based SMT setting. We show that a subword-level pivot-based SMT model using a related pivot language is substantially better than word and morphemelevel pivot models. It is also highly competitive with the best direct translation model, which is encouraging as no direct source-target training corpus is us...
متن کاملUtilizing Lexical Similarity for pivot translation involving resource-poor, related languages
We investigate pivot-based translation between related languages in a low resource, phrase-based SMT setting. We show that a subword-level pivot-based SMT model using a related pivot language is substantially better than word and morphemelevel pivot models. It is also highly competitive with the best direct translation model, which is encouraging as no direct source-target training corpus is us...
متن کاملEfficient Solutions for Word Reordering in German-English Phrase-Based Statistical Machine Translation
Despite being closely related languages, German and English are characterized by important word order differences. Longrange reordering of verbs, in particular, represents a real challenge for state-of-theart SMT systems and is one of the main reasons why translation quality is often so poor in this language pair. In this work, we review several solutions to improve the accuracy of German-Engli...
متن کاملFast, Scalable Phrase-Based SMT Decoding
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-exam...
متن کاملDocument-Wide Decoding for Phrase-Based Statistical Machine Translation
Independence between sentences is an assumption deeply entrenched in the models and algorithms used for statistical machine translation (SMT), particularly in the popular dynamic programming beam search decoding algorithm. This restriction is an obstacle to research on more sophisticated discourse-level models for SMT. We propose a stochastic local search decoding method for phrase-based SMT, w...
متن کامل