Download or read book Grammar Inference and Statistical Machine Translation written by Ye-Yi Wang and published by . This book was released on 1998 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "NLP researchers face a dilemma: on one side, it is unarguably accepted that languages have internal structure rather than strings of words. On the other side, they find it very difficult and expensive to write grammars that have good coverage of language structures. Statistical machine translation tries to cope with this problem by ignoring language structures and using a statistical models [sic] to depict the translation process. Most of the translation models are word-based. While the approach has achieved surprisingly good performance comparable to the best commercial systems, many questions remain in the machine translation community. Can the statistical word-based translation still perform well on language pairs with radically different linguistic structures? How would it function with less training data or with spoken languages? The thesis work investigated these questions. In summary, word-based alignment model is a major cause of errors in German-English statistical spoken language translation. To account for this problem, a structure-based alignment model is introduced. This new model takes advantages of a bilingual grammar inference algorithm, which can automatically acquire shallow phrase structures used by the model. The structure-based model can directly depict the structure difference between English and German spoken languages. It also results in focused learning of word alignment, therefore it can alleviate the sparse data problem. The structure-based model achieved 11 percent error reduction over the state-of-the-art statistical machine translation models."
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 Grammatical Inference Algorithms and Applications written by Yasibumi Sakaibara and published by Springer Science & Business Media. This book was released on 2006-09-18 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Colloquium on Grammatical Inference, ICGI 2006. The book presents 25 revised full papers and 8 revised short papers together with 2 invited contributions, carefully reviewed and selected. The topics discussed range from theoretical results of learning algorithms to innovative applications of grammatical inference and from learning several interesting classes of formal grammars to applications to natural language processing.
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 Syntax based Language Models for Statistical Machine Translation written by Matt Post and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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
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 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Grammatical Inference Algorithms and Applications written by Georgios Paliouras and published by Springer Science & Business Media. This book was released on 2004-10-05 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Colloquium on Grammatical Inference, ICGI 2004, held in Athens, Greece in October 2004. The 20 revised full papers and 8 revised poster papers presented together with 3 invited contributions were carefully reviewed and selected from 45 submissions. The topics of the papers presented range from theoretical results of learning algorithms to innovative applications of grammatical inference and from learning several interesting classes of formal grammars to estimations of probabilistic grammars.
Download or read book Coarse to Fine Natural Language Processing written by Slav Petrov and published by Springer Science & Business Media. This book was released on 2011-11-03 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: The impact of computer systems that can understand natural language will be tremendous. To develop this capability we need to be able to automatically and efficiently analyze large amounts of text. Manually devised rules are not sufficient to provide coverage to handle the complex structure of natural language, necessitating systems that can automatically learn from examples. To handle the flexibility of natural language, it has become standard practice to use statistical models, which assign probabilities for example to the different meanings of a word or the plausibility of grammatical constructions. This book develops a general coarse-to-fine framework for learning and inference in large statistical models for natural language processing. Coarse-to-fine approaches exploit a sequence of models which introduce complexity gradually. At the top of the sequence is a trivial model in which learning and inference are both cheap. Each subsequent model refines the previous one, until a final, full-complexity model is reached. Applications of this framework to syntactic parsing, speech recognition and machine translation are presented, demonstrating the effectiveness of the approach in terms of accuracy and speed. The book is intended for students and researchers interested in statistical approaches to Natural Language Processing. Slav’s work Coarse-to-Fine Natural Language Processing represents a major advance in the area of syntactic parsing, and a great advertisement for the superiority of the machine-learning approach. Eugene Charniak (Brown University)
Download or read book Grammatical Inference Algorithms and Applications written by Arlindo L. Oliveira and published by Springer. This book was released on 2004-02-13 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Colloquium on Grammatical Inference, ICGI 2000, held in Lisbon, Portugal in September 2000. The 24 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers address topics like machine learning, automata, theoretical computer science, computational linguistics, pattern recognition, artificial neural networks, natural language acquisition, computational biology, information retrieval, text processing, and adaptive intelligent agents.
Download or read book Statistical Language Learning written by Eugene Charniak and published by MIT Press. This book was released on 1996 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text introduces statistical language processing techniques--word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic word classes, word-sense disambiguation--along with the underlying mathematics and chapter exercises.
Download or read book Grammatical Inference Algorithms and Applications written by Arlindo L. Oliveira and published by Springer Science & Business Media. This book was released on 2000-09 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Colloquium on Grammatical Inference, ICGI 2000, held in Lisbon, Portugal in September 2000. The 24 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers address topics like machine learning, automata, theoretical computer science, computational linguistics, pattern recognition, artificial neural networks, natural language acquisition, computational biology, information retrieval, text processing, and adaptive intelligent agents.
Download or read book Challenges for Arabic Machine Translation written by Abdelhadi Soudi and published by John Benjamins Publishing. This book was released on 2012 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Grammatical Inference written by Vasant Honavar and published by Springer Science & Business Media. This book was released on 1998-07 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Fourth International Colloquium on Grammatical Inference, ICGI-98, held in Ames, Iowa, in July 1998. The 23 revised full papers were carefully reviewed and selected for inclusion in the book from a total of 35 submissions. The book addresses a wide range of grammatical inference theory such as automata induction, grammar induction, automatic language acquisition, etc. as well as a variety of applications in areas like syntactic pattern recognition, adaptive intelligent agents, diagnosis, computational biology, data mining, and knowledge discovery.
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 Quality Estimation for Machine Translation written by Lucia Specia and published by Springer Nature. This book was released on 2022-05-31 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many applications within natural language processing involve performing text-to-text transformations, i.e., given a text in natural language as input, systems are required to produce a version of this text (e.g., a translation), also in natural language, as output. Automatically evaluating the output of such systems is an important component in developing text-to-text applications. Two approaches have been proposed for this problem: (i) to compare the system outputs against one or more reference outputs using string matching-based evaluation metrics and (ii) to build models based on human feedback to predict the quality of system outputs without reference texts. Despite their popularity, reference-based evaluation metrics are faced with the challenge that multiple good (and bad) quality outputs can be produced by text-to-text approaches for the same input. This variation is very hard to capture, even with multiple reference texts. In addition, reference-based metrics cannot be used in production (e.g., online machine translation systems), when systems are expected to produce outputs for any unseen input. In this book, we focus on the second set of metrics, so-called Quality Estimation (QE) metrics, where the goal is to provide an estimate on how good or reliable the texts produced by an application are without access to gold-standard outputs. QE enables different types of evaluation that can target different types of users and applications. Machine learning techniques are used to build QE models with various types of quality labels and explicit features or learnt representations, which can then predict the quality of unseen system outputs. This book describes the topic of QE for text-to-text applications, covering quality labels, features, algorithms, evaluation, uses, and state-of-the-art approaches. It focuses on machine translation as application, since this represents most of the QE work done to date. It also briefly describes QE for several other applications, including text simplification, text summarization, grammatical error correction, and natural language generation.
Download or read book Neural Machine Translation written by Philipp Koehn and published by Cambridge University Press. This book was released on 2020-06-18 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.
