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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.

Record type: Conference or Workshop Item (Paper)

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.

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More information

Published date: October 2014
Venue - Dates: Empirical Methods on Natural Language Processing 2014, Qatar, 2014-10-25 - 2014-10-29
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Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 367606
URI: http://eprints.soton.ac.uk/id/eprint/367606
PURE UUID: 2a8a176c-4847-465d-806b-63486e5f3328

Catalogue record

Date deposited: 20 Aug 2014 15:30
Last modified: 18 Jul 2017 01:57

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Contributors

Author: Abdullah Alrajeh

University divisions

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