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method ID: discriminativelearning

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Domain Adaptation with Structural Correspondence Learning

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Publication: TIPSTER (2006)

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Discriminative learning methods are widely used in natural language process- ing. These methods work best when their training and test data are drawn from the same distribution. For many NLP …...

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Context-aware Discriminative Phrase Selection for Statistical Machine Translation

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Publication: TIPSTER (2007)

DOI: nan

In this work we revise the application of discriminative learning to the problem of phrase selection in Statistical Machine Translation. Inspired by common tech- niques used in Word Sense Disambiguation, …...

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Refining Generative Language Models using Discriminative Learning

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Publication: TIPSTER (2008)

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We propose a new approach to language mod- eling which utilizes discriminative learning methods. Our approach is an iterative one: starting with an initial language model, in each iteration we …...

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Consistent Translation using Discriminative Learning - A Translation Memory-inspired Approach

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We present a discriminative learning method to improve the consistency of translations in phrase-based Statistical Machine Translation (SMT) systems. Our method is inspired by Translation Memory (TM) systems which are …...

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A fully discriminative training framework for Statistical Machine Translation (Un cadre d’apprentissage intégralement discriminant pour la traduction statistique) [in French]

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A fully discriminative training framework for Statistical Machine Translation A major pitfall of existing statistical machine translation systems is their lack of a proper training procedure. In fact, the phrase …...

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Discriminative Learning with Natural Annotations: Word Segmentation as a Case Study

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Publication: TIPSTER (2013)

DOI: nan

Structural information in web text pro- vides natural annotations for NLP prob- lems such as word segmentation and pars- ing. In this paper we propose a discrim- inative learning algorithm …...

40216

Large-Scale Discriminative Training for Statistical Machine Translation Using Held-Out Line Search

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Publication: TIPSTER (2013)

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We introduce a new large-scale discrimina- tive learning algorithm for machine translation that is capable of learning parameters in mod- els with extremely sparse features. To ensure their reliable estimation …...

23718

Predicting Interesting Things in Text

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Publication: TIPSTER (2014)

DOI: nan

While reading a document, a user may encounter concepts, entities, and topics that she is interested in exploring more. We propose models of “interestingness”, which aim to predict the level …...

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Generation from Abstract Meaning Representation using Tree Transducers

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Publication: TIPSTER (2016)

DOI: nan

Meaning Representation using Tree Transducers Jeffrey Flanigan♠ Chris Dyer♠ Noah A. Smith♥ Jaime Carbonell♠ ♠School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA ♥Computer Science & Engineering, University of …...

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Headline Token-based Discriminative Learning for Subheading Generation in News Article

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AbstractThe news subheading summarizes an article’s contents in several sentences to support the headline limited to solely conveying the main contents. So, it is necessary to generate compelling news subheadings …...

33291