Large-scale reordering model for statistical machine translation using dual multinomial logistic regression


Alrajeh, Abdullah and Niranjan, Mahesan (2014) Large-scale reordering model for statistical machine translation using dual multinomial logistic regression At Empirical Methods on Natural Language Processing 2014, Qatar. 25 - 29 Oct 2014.

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Description/Abstract

Phrase reordering is a challenge for statistical machine translation systems. Posing phrase movements as a prediction problem using contextual features modeled by maximum entropy-based classifier is superior to the commonly used lexicalized reordering model. However, Training this discriminative model using large-scale parallel corpus might be computationally expensive. In this paper, we explore recent advancements in solving large-scale classification problems. Using the dual problem to multinomial logistic regression, we managed to shrink the training data while iterating and produce significant saving in computation and memory while preserving the accuracy.

Item Type: Conference or Workshop Item (Paper)
Venue - Dates: Empirical Methods on Natural Language Processing 2014, Qatar, 2014-10-25 - 2014-10-29
Related URLs:
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Organisations: Electronics & Computer Science
ePrint ID: 367606
Date :
Date Event
October 2014Published
Date Deposited: 20 Aug 2014 15:30
Last Modified: 17 Apr 2017 13:18
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/367606

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