Stochastic K-TSS Bi-Languages for Machine Translation
نویسندگان
چکیده
One of the approaches to statistical machine translation is based on joint probability distributions over some source and target languages. In this work we propose to model the joint probability distribution by stochastic regular bi-languages. Specifically we introduce the stochastic k-testable in the strict sense bi-languages to represent the joint probability distribution of source and target languages. With this basis we present a reformulation of the GIATI methodology to infer stochastic regular bi-languages for machine translation purposes.
منابع مشابه
Stochastic Bi-Languages to model Dialogs
Partially observable Markov decision Processes provide an excellent statistical framework to deal with spoken dialog systems that admits global optimization and deal with uncertainty of user goals. However its put in practice entails intractable problems that need efficient and suboptimal approaches. Alternatively some pattern recognition techniques have also been proposed. In this framework th...
متن کاملThe Correlation of Machine Translation Evaluation Metrics with Human Judgement on Persian Language
Machine Translation Evaluation Metrics (MTEMs) are the central core of Machine Translation (MT) engines as they are developed based on frequent evaluation. Although MTEMs are widespread today, their validity and quality for many languages is still under question. The aim of this research study was to examine the validity and assess the quality of MTEMs from Lexical Similarity set on machine tra...
متن کاملShallow Transfer Between Slavic Languages
This paper describes an architecture of a machine translation system designed primarily for Slavic languages. The architecture is based upon a shallow transfer module and a stochastic ranker. The shallow transfer module helps to resolve the problems, which arise even in the translation of related languages, the stochastic ranker then chooses the best translation out of a set provided by a shall...
متن کاملImproved Statistical Machine Translation for Resource-Poor Languages Using Related Resource-Rich Languages
We propose a novel language-independent approach for improving statistical machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resourcepoor source language X1 into a resourcerich language Y given a bi-text containing a limited number of parallel sentences for X1-Y and a larger bi-text for X2-Y fo...
متن کاملRecent Advances in Natural Language Processing
Recent developments in statistical machine translation (SMT), e.g., the availability of efficient implementations of integrated open-source toolkits like Moses, have made it possible to build a prototype system with decent translation quality for any language pair in a few days or even hours. This is so in theory. In practice, doing so requires having a large set of parallel sentence-aligned bi...
متن کامل