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	statistical machine translation </term> . This	statistical	approach aims to minimize <term> expected	#6557 This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance.
other,11-5-P03-1031,bq	this <term> ambiguity </term> based on <term>	statistical	information </term> obtained from <term> dialogue	#4227 This paper proposes a method for resolving this ambiguity based onstatistical information obtained from dialogue corpora.
other,14-1-P01-1070,bq	experiments centering on the construction of <term>	statistical	models </term> of <term> WH-questions </term>	#2138 We describe a set of supervised machine learning experiments centering on the construction ofstatistical models of WH-questions.
other,16-4-P05-1048,bq	including inherent limitations of current <term>	statistical	MT architectures </term> . <term> Syntax-based	#9404 Error analysis suggests several key factors behind this surprising finding, including inherent limitations of currentstatistical MT architectures.
other,8-3-H90-1016,bq	we use a <term> fully-connected first-order	statistical	class grammar </term> . The <term> speech-search	#16913 To avoid grammar coverage problems we use a fully-connected first-order statistical class grammar.
other,9-4-I05-2048,bq	intended to give an introduction to <term>	statistical	machine translation </term> with a focus	#8066 This workshop is intended to give an introduction tostatistical machine translation with a focus on practical considerations.
tech,0-1-P05-1067,bq	architectures </term> . <term> Syntax-based	statistical	machine translation ( MT ) </term> aims at	#9409 Syntax-based statistical machine translation (MT) aims at applying statistical models to structured data.
tech,1-3-H05-1095,bq	word-aligned corpora </term> is proposed . A <term>	statistical	translation model </term> is also presented	#7371 Astatistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric.
tech,10-1-P05-1032,bq	data structure </term> for <term> phrase-based	statistical	machine translation </term> which allows	#9132 In this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memory than is required by current decoder implementations.
tech,10-1-P05-1067,bq	translation ( MT ) </term> aims at applying <term>	statistical	models </term> to <term> structured data </term>	#9418 Syntax-based statistical machine translation (MT) aims at applyingstatistical models to structured data.
tech,10-2-P05-3025,bq	a <term> model </term> of <term> syntax-based	statistical	machine translation ( MT ) </term> , to understand	#9859 The method allows a user to explore a model of syntax-based statistical machine translation (MT), to understand the model's strengths and weaknesses, and to compare it to other MT systems.
tech,10-7-A94-1011,bq	repeat results which show that standard <term>	statistical	models </term> are not particularly suitable	#20106 We then proceed to repeat results which show that standardstatistical models are not particularly suitable for exploiting linguistically sophisticated representations, and show that a statistically fitted rule-based model provides significantly improved performance for sophisticated representations.
tech,11-1-N03-2036,bq	phrase-based unigram model </term> for <term>	statistical	machine translation </term> that uses a much	#3400 In this paper, we describe a phrase-based unigram model forstatistical machine translation that uses a much simpler set of model parameters than similar phrase-based models.
tech,11-5-N04-1022,bq	decoding </term> can be used to tune <term>	statistical	MT </term> performance for specific <term>	#6637 Our results show that MBR decoding can be used to tunestatistical MT performance for specific loss functions.
tech,13-3-I05-2021,bq	improvements in the <term> BLEU scores </term> of <term>	statistical	machine translation ( SMT ) </term> suggests	#7869 At the same time, the recent improvements in the BLEU scores ofstatistical machine translation (SMT) suggests that SMT models are good at predicting the right translation of the words in source language sentences.
tech,15-3-A94-1011,bq	is presented which involves using a <term>	statistical	POS tagger </term> in conjunction with <term>	#19960 A novel method for adding linguistic annotation to corpora is presented which involves using astatistical POS tagger in conjunction with unsupervised structure finding methods to derive notions of noun group, verb group, and so on which is inherently extensible to more sophisticated annotation, and does not require a pre-tagged corpus to fit.
tech,16-1-P05-1048,bq	sense disambigation models </term> help <term>	statistical	machine translation </term><term> quality	#9327 We directly investigate a subject of much recent debate: do word sense disambigation models helpstatistical machine translation quality?
tech,17-1-P05-1069,bq	phrase-based prediction model </term> for <term>	statistical	machine translation ( SMT ) </term> . The	#9566 In this paper, we present a novel training method for a localized phrase-based prediction model forstatistical machine translation (SMT).
tech,17-2-N03-1004,bq	mechanisms </term> and the other adopting <term>	statistical	techniques </term> . We present our <term>	#2369 The answering agents adopt fundamentally different strategies, one utilizing primarily knowledge-based mechanisms and the other adoptingstatistical techniques.
tech,18-1-A94-1011,bq	within the standard <term> term weighting	statistical	assignment paradigm </term> ( Fagan 1987	#19903 The use of NLP techniques for document classification has not produced significant improvements in performance within the standard term weighting statistical assignment paradigm (Fagan 1987; Lewis, 1992bc; Buckley, 1993).


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