An Incremental Syntactic Language Model for Statistical Phrase-based Translation PDF Download

Author: Lane Oscar Bingaman Schwartz
Publisher:
ISBN:
Category :
Languages : en
Pages : 238

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Author: Philip Williams
Publisher: Springer Nature
ISBN: 3031021649
Category : Computers
Languages : en
Pages : 190

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Book Description
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: Deyi Xiong
Publisher: Springer
ISBN: 9812873562
Category : Language Arts & Disciplines
Languages : en
Pages : 159

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Book Description
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: Peter Venable
Publisher:
ISBN:
Category : Computational linguistics
Languages : en
Pages : 128

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Book Description
Abstract: "Syntactic structure is an important component of natural language utterances, for both form and content. Therefore, a variety of applications can benefit from the integration of syntax into their statistical models of language. In this thesis, two new syntax-based models are presented, along with their training algorithms: a monolingual generative model of sentence structure, and a model of the relationship between the structure of a sentence in one language and the structure of its translation into another language. After these models are trained and tested on the respective tasks of monolingual parsing and word-level bilingual corpus alignment, they are demonstrated in two additional applications. First, a new statistical parser is automatically induced for a language in which none was available, using a bilingual corpus. Second, a statistical translation system is augmented with syntax-based models. Thus the contributions of this thesis include: a statistical parsing system; a bilingual parsing system, which infers a structural relationship between two languages using a bilingual corpus; a method for automatically building a parser for a language where no parser is available; and a translation model that incorporates phrase structure."

Author: Ming Tan
Publisher:
ISBN:
Category : Computer science
Languages : en
Pages : 110

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Book Description
The n-gram model is the most widely used language model (LM) in statistical machine translation system, due to its simplicity and scalability. However, it only encodes the local lexical relation between adjacent words and clearly ignores the rich syntactic and semantic structures of the natural languages. Attempting to increase the order of an n-gram to describe longer range dependencies in natural language immediately runs into the curse of dimensionality. Although previous researches tried to increase the order of n-gram on a large corpus, they did not see obvious improvement beyond 6-gram. Meanwhile, other LMs, such as syntactic language models and topic language models, tried to encode the long range dependencies from different perspectives of natural languages. But it is still an open question how to effectively combine those language models in order to capture multiple linguistic phenomena. This dissertation presents a study at building a large scale distributed composite language model that is formed by seamlessly combining an n-gram model, a structured language model, and probabilistic latent semantic analysis under a directed Markov random field paradigm to simultaneously account for local word lexical information, mid-range sentence syntactic structure, and long-span document semantic content. The composite language model has been trained by performing a convergent N-best list approximate EM algorithm and a follow-up EM algorithm. To improve word prediction power, the composite LM is distributed with client-server paradigm and trained on corpora with up to a billion tokens. Also, the orders of the composite LM are increased up to 5-gram and 4-headword. The large scale distributed composite language model gives drastic perplexity reduction over n-grams and achieves significantly better translation quality measured by the BLEU score and "readability" of translations when applied to the task of re-ranking the N-best list from a state-of-the-art parsing-based machine translation system. Moreover, we propose an A*-search-based lattice rescoring strategy in order to integrate the large scale distributed composite language model into a phrase-based machine translation system. Experiments show that the A*-based lattice re-scoring is more effective to show the predominance of the composite language model over the n-gram model than the traditional N-best list re-scoring.

Author: Abdelhadi Soudi
Publisher: John Benjamins Publishing
ISBN: 9027273626
Category : Language Arts & Disciplines
Languages : en
Pages : 167

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Book Description
This book is the first volume that focuses on the specific challenges of machine translation with Arabic either as source or target language. It nicely fills a gap in the literature by covering approaches that belong to the three major paradigms of machine translation: Example-based, statistical and knowledge-based. It provides broad but rigorous coverage of the methods for incorporating linguistic knowledge into empirical MT. The book brings together original and extended contributions from a group of distinguished researchers from both academia and industry. It is a welcome and much-needed repository of important aspects in Arabic Machine Translation such as morphological analysis and syntactic reordering, both central to reducing the distance between Arabic and other languages. Most of the proposed techniques are also applicable to machine translation of Semitic languages other than Arabic, as well as translation of other languages with a complex morphology.

Author: Jan W. Amtrup
Publisher: Springer
ISBN: 3540467610
Category : Computers
Languages : en
Pages : 213

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Book Description
Human language capabilities are based on mental proceduresthat are closely linked to the time domain. Listening, understanding,and reacting, on the one hand, as well as planning,formulating,and speaking,onthe other, are performedin a highlyover lapping manner, thus allowing inter human communication to proceed in a smooth and ?uent way. Although it happens to be the natural mode of human language interaction, in cremental processing is still far from becoming a common feature of today’s lan guage technology. Instead, it will certainly remain one of the big challenges for research activities in the years to come. Usually considered dif?cult to a degree that rendersit almost intractableforpracticalpurposes,incrementallanguageprocessing has recently been attracting a steadily growing interest in the spoken language pro cessing community. Its notorious dif?culty can be attributed mainly to two reasons: Due to the inaccessibility of the right context, global optimization criteria are no longer available. This loss must be compensated for by communicating larger search spaces between system components or by introducing appropriate repair mechanisms. In any case, the complexity of the task can easily grow by an order of magnitude or even more. Incrementality is an almost useless feature as long as it remains a local property of individual system components. The advantages of incremental processing can be effectiveonly if all the componentsof a producer consumerchain consistently adhere to the same pattern of temporal behavior.

Author: Philipp Koehn
Publisher: Cambridge University Press
ISBN: 0521874157
Category : Computers
Languages : en
Pages : 447

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Book Description
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: Matthew John Post
Publisher:
ISBN:
Category :
Languages : en
Pages : 268

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Book Description
"The goal of machine translation is to develop algorithms that produce human-quality translations of natural language sentences. The evaluation of machine translation quality is split broadly into two aspects: adequacy and fluency. Adequacy measures how faithfully the meaning of the original sentence is preserved, whereas fluency measures whether this meaning is expressed in valid sentences in the target language. While both of these criteria are difficult to meet, fluency is a much more difficult goal. Generally, this likely has something to do with the asymmetrical nature of producing and understanding sentences; although humans are quite robust at inferring the meaning of text even in the presence of lots of noise and error, the rules that govern grammatical utterances are exacting, subtle, and elusive. To produce understandable text, we can rely on this robust processing hardware, but to produce grammatical text, we have to understand how it works. This dissertation attempts to improve the fluency of machine translation output by explicitly incorporating models of the target language structure into machine translation systems. It is organized into three parts. First, we propose a framework for decoding that decouples the structures of the sentences of the source and target languages, and evaluate it with existing grammatical models as language models for machine translation. Next, we apply lessons from that task to the learning of grammars more suitable to the demands of the machine translation. We then incorporate these grammars, called Tree Substitution Grammars, into our decoding framework.--Leaf vi.

Author: Jan W. Amtrup
Publisher: Springer Science & Business Media
ISBN: 9783540667537
Category : Computers
Languages : en
Pages : 228

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Book Description
This book describes a complete translation system for spontaneously spoken language, constructed using the incremental paradigm. It starts by presenting the theoretical and algorithmic basis necessary to cope with the complex endeavour of translating speech incrementally and in parallel. In particular, graph-theoretic foundations of natural language processing and feature-based descriptions of linguistic objects are covered. A thorough description of the system and its performance follows. The author covers syntactic and semantic processing as well as transfer and syntactic generation. Thus the book can also be used as a broad-coverage introduction to the field of speech translation. This book is essential reading for researchers and students working in the field of speech translation. It is also intended as a research tool for those interested in the architecture of general natural language processing systems.