Download or read book Statistical Machine Translation Beyond Context free Grammar written by Miriam Käshammer and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Syntax based Statistical Machine Translation written by Philip Williams and published by Springer Nature. This book was released on 2022-05-31 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Linguistically Motivated Statistical Machine Translation written by Deyi Xiong and published by Springer. This book was released on 2015-02-11 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computer Processing of Oriental Languages Beyond the Orient The Research Challenges Ahead written by Yuji Matsumoto and published by Springer. This book was released on 2006-11-28 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 21st International Conference on Computer Processing of Oriental Languages, ICCPOL 2006, held in Singapore in December 2006, co-located with ISCSLP 2006, the 5th International Symposium on Chinese Spoken Language Processing. Coverage includes information retrieval, machine translation, word segmentation, abbreviation expansion, writing-system issues, semantics, and lexical resources.

Download or read book Statistical Machine Translation written by Philipp Koehn and published by Cambridge University Press. This book was released on 2010 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Improvements in Hierarchical Phrase based Statistical Machine Translation written by Baskaran Sankaran and published by . This book was released on 2013 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).

Download or read book Machine Translation with Minimal Reliance on Parallel Resources written by George Tambouratzis and published by Springer. This book was released on 2017-08-09 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unified view on a new methodology for Machine Translation (MT). This methodology extracts information from widely available resources (extensive monolingual corpora) while only assuming the existence of a very limited parallel corpus, thus having a unique starting point to Statistical Machine Translation (SMT). In this book, a detailed presentation of the methodology principles and system architecture is followed by a series of experiments, where the proposed system is compared to other MT systems using a set of established metrics including BLEU, NIST, Meteor and TER. Additionally, a free-to-use code is available, that allows the creation of new MT systems. The volume is addressed to both language professionals and researchers. Prerequisites for the readers are very limited and include a basic understanding of the machine translation as well as of the basic tools of natural language processing.​

Download or read book Distributed Computing and Artificial Intelligence written by Andre Ponce de Leon F. de Carvalho and published by Springer Science & Business Media. This book was released on 2010-11-18 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Symposium on Distributed Computing and Artificial Intel- gence (DCAI ́10) is an annual forum that brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application to provide efficient solutions to real problems. This symposium is organized by the Biomedicine, Intelligent System and Edu- tional Technology Research Group (http://bisite. usal. es/) of the University of - lamanca. The present edition has been held at the Polytechnic University of - lencia, from 7 to 10 September 2010, within the Congreso Español de Informática (CEDI 2010). Technology transfer in this field is still a challenge, with a large gap between academic research and industrial products. This edition of DCAI aims at contributing to reduce this gap, with a stimulating and productive forum where these communities can work towards future cooperation with social and econo- cal benefits. This conference is the forum in which to present application of in- vative techniques to complex problems. Artificial intelligence is changing our - ciety. Its application in distributed environments, such as internet, electronic commerce, environment monitoring, mobile communications, wireless devices, distributed computing, to cite some, is continuously increasing, becoming an e- ment of high added value with social and economic potential, both industry, life quality and research. These technologies are changing constantly as a result of the large research and technical effort being undertaken in universities, companies.

Download or read book Computational Linguistics and Intelligent Text Processing written by Alexander Gelbukh and published by Springer. This book was released on 2010-03-17 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: th CICLing 2010 was the 11 Annual Conference on Intelligent Text Processing and Computational Linguistics. The CICLing conferences provide a wide-scope forum for discussion of the art and craft of natural language processing research as well as the best practices in its applications. This volume contains three invited papers and the regular papers accepted for oral presentation at the conference. The papers accepted for poster pres- tation were published in a special issue of another journal (see information on thewebsite).Since 2001,theproceedingsofCICLingconferenceshavebeen p- lished in Springer’s Lecture Notes in Computer Science series, as volumes 2004, 2276, 2588, 2945, 3406, 3878, 4394, 4919, and 5449. The volume is structured into 12 sections: – Lexical Resources – Syntax and Parsing – Word Sense Disambiguation and Named Entity Recognition – Semantics and Dialog – Humor and Emotions – Machine Translation and Multilingualism – Information Extraction – Information Retrieval – Text Categorization and Classi?cation – Plagiarism Detection – Text Summarization – Speech Generation The 2010 event received a record high number of submissions in the - year history of the CICLing series. A total of 271 papers by 565 authors from 47 countriesweresubmittedforevaluationbytheInternationalProgramCommittee (see Tables 1 and 2). This volume contains revised versions of 61 papers, by 152 authors, selected for oral presentation; the acceptance rate was 23%.

Download or read book Statistical Machine Translation written by and published by . This book was released on 2006 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nonlinear Speech Modeling and Applications written by Gerard Chollet and published by Springer. This book was released on 2005-07-12 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the revised tutorial lectures given at the International Summer School on Nonlinear Speech Processing-Algorithms and Analysis held in Vietri sul Mare, Salerno, Italy in September 2004. The 14 revised tutorial lectures by leading international researchers are organized in topical sections on dealing with nonlinearities in speech signals, acoustic-to-articulatory modeling of speech phenomena, data driven and speech processing algorithms, and algorithms and models based on speech perception mechanisms. Besides the tutorial lectures, 15 revised reviewed papers are included presenting original research results on task oriented speech applications.

Download or read book Learning for Semantic Parsing Using Statistical Machine Translation Techniques written by and published by . This book was released on 2005 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: Semantic parsing is the construction of a complete, formal, symbolic meaning representation of a sentence. While it is crucial to natural language understanding, the problem of semantic parsing has received relatively little attention from the machine learning community. Recent work on natural language understanding has mainly focused on shallow semantic analysis, such as word-sense disambiguation and semantic role labeling. Semantic parsing, on the other hand, involves deep semantic analysis in which word senses, semantic roles and other components are combined to produce useful meaning representations for a particular application domain (e.g. database query). Prior research in machine learning for semantic parsing is mainly based on inductive logic programming or deterministic parsing, which lack some of the robustness that characterizes statistical learning. Existing statistical approaches to semantic parsing, however, are mostly concerned with relatively simple application domains in which a meaning representation is no more than a single semantic frame. In this proposal, we present a novel statistical approach to semantic parsing, WASP, which can handle meaning representations with a nested structure. The WASP algorithm learns a semantic parser given a set of sentences annotated with their correct meaning representations. The parsing model is based on the synchronous context-free grammar, where each rule maps a natural-language substring to its meaning representation. The main innovation of the algorithm is its use of state-of-the-art statistical machine translation techniques. A statistical word alignment model is used for lexical acquisition, and the parsing model itself can be seen as an instance of a syntax-based translation model. In initial evaluation on several real-world data sets, we show that WASP performs favorably in terms of both accuracy and coverage compared to existing learning methods requiring similar amount of supervision, and shows better robustness to variations in task complexity and word order. In future work, we intend to pursue several directions in developing accurate semantic parsers for a variety of application domains. This will involve exploiting prior knowledge about the natural-language syntax and the application domain. We also plan to construct a syntax-aware word-based alignment model for lexical acquisition. Finally, we will generalize the learning algorithm to handle context-dependent sentences and accept noisy training data.

Download or read book Machine Translation written by Sergei Nirenburg and published by Morgan Kaufmann. This book was released on 1992 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: All over the world, people are claiming their rights. Are these claims prompted by similar values and aspirations? And even if human rights are universal, what are the consequences of claiming them in different historical, cultural and material realities? The diversity of African countries considered in this book compels careful thought about these questions.

Download or read book Grammatical Inference Theoretical Results and Applications written by José Sempere and published by Springer Science & Business Media. This book was released on 2010-09-03 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Colloquium on Grammatical Inference, ICGI 2010, held in Valencia, Spain, in September 2010. The 18 revised full papers and 14 revised short papers presented were carefully reviewed and selected from numerous submissions. The topics of the papers presented vary from theoretical results about the learning of different formal language classes (regular, context-free, context-sensitive, etc.) to application papers on bioinformatics, language modelling or software engineering. Furthermore there are two invited papers on the topics grammatical inference and games and molecules, languages, and automata.

Download or read book ACL 2007 written by Association for Computational Linguistics. Meeting and published by . This book was released on 2007 with total page 1098 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Modeling Relevance in Statistical Machine Translation written by Aaron B. Phillips and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Use of Source Language Context in Statistical MacHine Translation written by Rejwanul Haque and published by LAP Lambert Academic Publishing. This book was released on 2012-02 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: The translation features typically used in state-of-the-art statistical machine translation (SMT) model dependencies between the source and target phrases, but not among the phrases in the source language themselves. A swathe of research has demonstrated that integrating source context modelling directly into log-linear phrase-based SMT (PB-SMT) and hierarchical PB-SMT (HPB-SMT), and can positively influence the weighting and selection of target phrases, and thus improve translation quality. In this book we present novel approaches to incorporate source-language contextual modelling into the state-of-the-art SMT models in order to enhance the quality of lexical selection. We investigate the effectiveness of use of a range of contextual features, including lexical features of neighbouring words, part-of-speech tags, supertags, sentence-similarity features, dependency information, and semantic roles. We explored a series of language pairs featuring typologically different languages, and examined the scalability of our research to larger amounts of training data.