Distance phrase reordering for moses - user manual and code guide

Y Ni; M Niranjan; C Saunders; S Szedmak

Distance phrase reordering for moses - user manual and code guide

Y Ni, M Niranjan, C Saunders, S Szedmak

Research output: Working paper

Abstract

We describe the implementation of a novel distance phrase reordering (DPR) model for a public domain statistical machine translation (SMT) system - MOSES. The model mainly focuses on the application of machine learning (ML) techniques to a speciﬁc problem in machine translation: learning the grammatical rules and content dependent changes, which are simpliﬁed as phrase reorderings. This document serves two purposes: a user manual for the functions of the DPR model and a code guide for developers.

Translated title of the contribution	Distance phrase reordering for moses - user manual and code guide
Original language	English
Publisher	University of Southampton
Number of pages	43
Publication status	Published - 2010

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title = "Distance phrase reordering for moses - user manual and code guide",

abstract = "We describe the implementation of a novel distance phrase reordering (DPR) model for a public domain statistical machine translation (SMT) system - MOSES. The model mainly focuses on the application of machine learning (ML) techniques to a speciﬁc problem in machine translation: learning the grammatical rules and content dependent changes, which are simpliﬁed as phrase reorderings. This document serves two purposes: a user manual for the functions of the DPR model and a code guide for developers.",

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AU - Ni, Y

AU - Niranjan, M

AU - Saunders, C

AU - Szedmak, S

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N2 - We describe the implementation of a novel distance phrase reordering (DPR) model for a public domain statistical machine translation (SMT) system - MOSES. The model mainly focuses on the application of machine learning (ML) techniques to a speciﬁc problem in machine translation: learning the grammatical rules and content dependent changes, which are simpliﬁed as phrase reorderings. This document serves two purposes: a user manual for the functions of the DPR model and a code guide for developers.

AB - We describe the implementation of a novel distance phrase reordering (DPR) model for a public domain statistical machine translation (SMT) system - MOSES. The model mainly focuses on the application of machine learning (ML) techniques to a speciﬁc problem in machine translation: learning the grammatical rules and content dependent changes, which are simpliﬁed as phrase reorderings. This document serves two purposes: a user manual for the functions of the DPR model and a code guide for developers.

M3 - Working paper

BT - Distance phrase reordering for moses - user manual and code guide

PB - University of Southampton

ER -

Distance phrase reordering for moses - user manual and code guide

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