Improvements In Hierarchical Phrase Based Statistical Machine Translation
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Author |
: Baskaran Sankaran |
Publisher |
: |
Total Pages |
: 133 |
Release |
: 2013 |
ISBN-10 |
: OCLC:1125865707 |
ISBN-13 |
: |
Rating |
: 4/5 (07 Downloads) |
Synopsis Improvements in Hierarchical Phrase-based Statistical Machine Translation by : Baskaran Sankaran
Hierarchical phrase-based translation (Hiero) is a statistical machine translation (SMT) model that encodes translation as a synchronous context-free grammar derivation between source and target language strings (Chiang, 2005; Chiang, 2007). Hiero models are more powerful than phrase-based models in capturing complex source-target reordering as well as discontiguous phrases, while being easier to estimate and decode with compared to their full syntax-based counterparts. In this thesis, we propose improvements to two broad aspects of the Hiero translation pipeline: i) learning Hiero translation model and estimating their parameters and ii) parameter tuning for discriminative log-linear models that are used to decode with such features. We use our own open-source implementation of Hiero called Kriya (Sankaran et al., 2012b) for all the experiments in this thesis. This thesis contains the following specific contributions: We propose a Bayesian model for learning Hiero grammars as an alternative to the heuristic method usually used in Hiero. Our model learns a peaked distribution of grammars, which consistently performs better than the heuristically extracted grammars across several language pairs (Sankaran et al., 2013a). We propose a novel unified-cascade framework for jointly learning alignments and the Hiero translation rules by removing the disconnect between the alignments and extracted synchronous context-free grammar. This is the first time a joint training framework is being proposed for Hiero, where we iterate the two step inference so that it learns in alternate iterations the phrase alignments and then the Hiero rules that are consistent with alignments. We extend our Bayesian model for extracting compact Hiero translation rules using arity-1 grammars, resulting in up to 57% reduction in model size while retaining the translation performance (Sankaran et al., 2011; Sankaran et al., 2012a). We propose several novel approaches for parameter tuning of discriminative log-linear models for SMT which can be used for jointly optimizing towards multiple evaluation metrics. We show that our methods for multi-objective tuning for SMT yield substantial gains in translation quality measured through automatic as well as human evaluations (Sankaran et al., 2013b; Duh et al., 2013).
Author |
: Hala Almaghout |
Publisher |
: |
Total Pages |
: |
Release |
: 2012 |
ISBN-10 |
: OCLC:900639137 |
ISBN-13 |
: |
Rating |
: 4/5 (37 Downloads) |
Synopsis CCG-augmented Hierarchical Phrase-based Statistical Machine Translation by : Hala Almaghout
Author |
: Philip Williams |
Publisher |
: Springer Nature |
Total Pages |
: 190 |
Release |
: 2022-05-31 |
ISBN-10 |
: 9783031021640 |
ISBN-13 |
: 3031021649 |
Rating |
: 4/5 (40 Downloads) |
Synopsis Syntax-based Statistical Machine Translation by : Philip Williams
This unique book provides a comprehensive introduction to the most popular syntax-based statistical machine translation models, filling a gap in the current literature for researchers and developers in human language technologies. While phrase-based models have previously dominated the field, syntax-based approaches have proved a popular alternative, as they elegantly solve many of the shortcomings of phrase-based models. The heart of this book is a detailed introduction to decoding for syntax-based models. The book begins with an overview of synchronous-context free grammar (SCFG) and synchronous tree-substitution grammar (STSG) along with their associated statistical models. It also describes how three popular instantiations (Hiero, SAMT, and GHKM) are learned from parallel corpora. It introduces and details hypergraphs and associated general algorithms, as well as algorithms for decoding with both tree and string input. Special attention is given to efficiency, including search approximations such as beam search and cube pruning, data structures, and parsing algorithms. The book consistently highlights the strengths (and limitations) of syntax-based approaches, including their ability to generalize phrase-based translation units, their modeling of specific linguistic phenomena, and their function of structuring the search space.
Author |
: Matthias Huck |
Publisher |
: |
Total Pages |
: |
Release |
: 2018 |
ISBN-10 |
: OCLC:1137060051 |
ISBN-13 |
: |
Rating |
: 4/5 (51 Downloads) |
Synopsis Statistical Models for Hierarchical Phrase-based Machine Translation by : Matthias Huck
Author |
: Philipp Koehn |
Publisher |
: Cambridge University Press |
Total Pages |
: 447 |
Release |
: 2010 |
ISBN-10 |
: 9780521874151 |
ISBN-13 |
: 0521874157 |
Rating |
: 4/5 (51 Downloads) |
Synopsis Statistical Machine Translation by : Philipp Koehn
The dream of automatic language translation is now closer thanks to recent advances in the techniques that underpin statistical machine translation. This class-tested textbook from an active researcher in the field, provides a clear and careful introduction to the latest methods and explains how to build machine translation systems for any two languages. It introduces the subject's building blocks from linguistics and probability, then covers the major models for machine translation: word-based, phrase-based, and tree-based, as well as machine translation evaluation, language modeling, discriminative training and advanced methods to integrate linguistic annotation. The book also reports the latest research, presents the major outstanding challenges, and enables novices as well as experienced researchers to make novel contributions to this exciting area. Ideal for students at undergraduate and graduate level, or for anyone interested in the latest developments in machine translation.
Author |
: Deyi Xiong |
Publisher |
: Springer |
Total Pages |
: 159 |
Release |
: 2015-02-11 |
ISBN-10 |
: 9789812873569 |
ISBN-13 |
: 9812873562 |
Rating |
: 4/5 (69 Downloads) |
Synopsis Linguistically Motivated Statistical Machine Translation by : Deyi Xiong
This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated SMT by enhancing the following three essential components: translation, reordering and bracketing models. It also serves the purpose of promoting the in-depth study of the impacts of linguistic knowledge on machine translation. Finally it provides a systematic introduction of Bracketing Transduction Grammar (BTG) based SMT, one of the state-of-the-art SMT formalisms, as well as a case study of linguistically motivated SMT on a BTG-based platform.
Author |
: Meng Ji |
Publisher |
: Cambridge University Press |
Total Pages |
: 285 |
Release |
: 2019-06-13 |
ISBN-10 |
: 9781108423274 |
ISBN-13 |
: 1108423272 |
Rating |
: 4/5 (74 Downloads) |
Synopsis Advances in Empirical Translation Studies by : Meng Ji
Introduces the integration of theoretical and applied translation studies for socially-oriented and data-driven empirical translation research.
Author |
: David Vilar Torres |
Publisher |
: |
Total Pages |
: 0 |
Release |
: 2011 |
ISBN-10 |
: OCLC:1075676671 |
ISBN-13 |
: |
Rating |
: 4/5 (71 Downloads) |
Synopsis Investigations on Hierarchical Phrase-based Machine Translation by : David Vilar Torres
Author |
: Sergei Nirenburg |
Publisher |
: IOS Press |
Total Pages |
: 338 |
Release |
: 1993 |
ISBN-10 |
: 905199074X |
ISBN-13 |
: 9789051990744 |
Rating |
: 4/5 (4X Downloads) |
Synopsis Progress in Machine Translation by : Sergei Nirenburg
Author |
: Philipp Koehn |
Publisher |
: Cambridge University Press |
Total Pages |
: 409 |
Release |
: 2020-06-18 |
ISBN-10 |
: 9781108497329 |
ISBN-13 |
: 1108497322 |
Rating |
: 4/5 (29 Downloads) |
Synopsis Neural Machine Translation by : Philipp Koehn
Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.