Download or read book Alignment Models and Algorithms for Statistical Machine Translation written by James Jonathan Jesse Brunning and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 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 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 On Word Alignment Models for Statistical Machine Translation written by Shaojun Zhao and published by . This book was released on 2011 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Machine translation remains the holy grail of computational linguistics. All statistical machine translation systems are built upon the idea of word alignment. While the field of word alignment has had tremendous progress in the last two decades, it is still in great need of speed and quality improvement. We designed a fertility hidden Markov model for word alignment, which is dramatically faster than the most widely used IBM Model 4. In fact, our model is even faster and has lower alignment error rate (AER) than the hidden Markov model. An experiment on Chinese-English translation shows that our word alignment model leads to better translation results than IBM Model 4, based on the BLEU metric. We also designed algorithms that mine massive and high quality bilingual texts for a variety of language pairs from the web using word alignment. The resulting data improved a state-ofthe- art machine translation system."--Leaf v.

Download or read book Phrase Alignment Models for Statistical Machine Translation written by John Sturdy DeNero and published by . This book was released on 2010 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of a machine translation (MT) system is to automatically translate a document written in some human input language (e.g., Mandarin Chinese) into an equivalent document written in an output language (e.g., English). This task--so simple in its specification, and yet so rich in its complexities--has challenged computer science researchers for 60 years. While MT systems are in wide use today, the problem of producing human-quality translations remains unsolved. Statistical approaches have substantially improved the quality of MT systems by effectively exploiting parallel corpora: large collections of documents that have been translated by people, and therefore naturally occur in both the input and output languages. Broadly characterized, statistical MT systems translate an input document by matching fragments of its contents to examples in a parallel corpus, and then stitching together the translations of those fragments into a coherent document in an output language. The central challenge of this approach is to distill example translations into reusable parts: fragments of sentences that we know how to translate robustly and are likely to recur. Individual words are certainly common enough to recur, but they often cannot be translated correctly in isolation. At the other extreme, whole sentences can be translated without much context, but rarely repeat, and so cannot be recycled to build new translations. This thesis focuses on acquiring translations of phrases: contiguous sequences of a few words that encapsulate enough context to be translatable, but recur frequently in large corpora. We automatically identify phrase-level translations that are contained within human-translated sentences by partitioning each sentence into phrases and aligning phrases across languages. This alignment-based approach to acquiring phrasal translations gives rise to statistical models of phrase alignment. A statistical phrase alignment model assigns a score to each possible analysis of a sentence-level translation, where an analysis describes which phrases within that sentence can be translated and how to translate them. If the model assigns a high score to a particular phrasal translation, we should be willing to reuse that translation in new sentences that contain the same phrase. Chapter 1 provides a non-technical introduction to phrase alignment models and machine translation. Chapter 2 describes a complete state-of-the-art phrase-based translation system to clarify the role of phrase alignment models. The remainder of this thesis presents a series of novel models, analyses, and experimental results that together constitute a thorough investigation of phrase alignment models for statistical machine translation. Chapter 3 presents the formal properties of the class of phrase alignment models, including inference algorithms and tractability results. We present two specific models, along with statistical learning techniques to fit their parameters to data. Our experimental evaluation identifies two primary challenges to training and employing phrase alignment models, and we address each of these in turn. The first broad challenge is that generative phrase models are structured to prefer very long, rare phrases. These models require external pressure to explain observed translations using small, reusable phrases rather than large, unique ones. Chapter 4 describes three Bayesian models and a corresponding Gibbs sampler to address this challenge. These models outperform the word-level models that are widely employed in research and production MT systems. The second broad challenge is structural: there are many consistent and coherent ways of analyzing a translated sentence using phrases. Long phrases, short phrases, and overlapping phrases can all simultaneously express correct, translatable units. However, no previous phrase alignment models have leveraged this rich structure to predict alignments. We describe a discriminative model of multi-scale, overlapping phrases that outperforms all previously proposed models. The cumulative result of this thesis is to establish model-based phrase alignment as the most effective approach to acquiring phrasal translations. Only phrase alignment models are able to incorporate statistical signals about multi-word constructions into alignment decisions and score coherent phrasal analyses of full sentence pairs. As a result, phrase alignment models outperform classical word-level models in both generative and discriminative settings. This result is fundamental to the field: the models proposed in this thesis address a general, language-independent alignment problem that arises in all state-of-the-art statistical machine translation systems in use today.

Download or read book Bitext Alignment written by Jörg Tiedemann and published by Morgan & Claypool Publishers. This book was released on 2011-05-05 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of various techniques for the alignment of bitexts. It describes general concepts and strategies that can be applied to map corresponding parts in parallel documents on various levels of granularity. Bitexts are valuable linguistic resources for many different research fields and practical applications. The most predominant application is machine translation, in particular, statistical machine translation. However, there are various other threads that can be followed which may be supported by the rich linguistic knowledge implicitly stored in parallel resources. Bitexts have been explored in lexicography, word sense disambiguation, terminology extraction, computer-aided language learning and translation studies to name just a few. The book covers the essential tasks that have to be carried out when building parallel corpora starting from the collection of translated documents up to sub-sentential alignments. In particular, it describes various approaches to document alignment, sentence alignment, word alignment and tree structure alignment. It also includes a list of resources and a comprehensive review of the literature on alignment techniques. Table of Contents: Introduction / Basic Concepts and Terminology / Building Parallel Corpora / Sentence Alignment / Word Alignment / Phrase and Tree Alignment / Concluding Remarks

Download or read book Verbmobil Foundations of Speech to Speech Translation written by Wolfgang Wahlster and published by Springer Science & Business Media. This book was released on 2000-07-31 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: Verbmobil is the result of eight years of intensive research in a large speech-to-speech translation project, executed by a consortium comprising nineteen academic and four industrial partners. The system that was developed by more than 100 researchers and engineers handles dialogs in three business-oriented domains, with translation between three languages: German, English, and Japanese. Verbmobil deals with spontaneous speech, which includes realistic repair phenomena, and uses deep semantic analysis to recognize a speaker's slips and to translate what he tried to say rather than what he actually said. - This book gives the first comprehensive overview of the results of this unique and seminal project in human language technology. Contributions by leading scientists in speech and language technology look at the component technologies that make Verbmobil the most advanced speech-to-speech translation system worldwide and a landmark project in the history of natural language processing.

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 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.

Download or read book Machine Translation and Transliteration involving Related Low resource Languages written by Anoop Kunchukuttan and published by CRC Press. This book was released on 2021-09-08 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Translation and Transliteration involving Related, Low-resource Languages discusses an important aspect of natural language processing that has received lesser attention: translation and transliteration involving related languages in a low-resource setting. This is a very relevant real-world scenario for people living in neighbouring states/provinces/countries who speak similar languages and need to communicate with each other, but training data to build supporting MT systems is limited. The book discusses different characteristics of related languages with rich examples and draws connections between two problems: translation for related languages and transliteration. It shows how linguistic similarities can be utilized to learn MT systems for related languages with limited data. It comprehensively discusses the use of subword-level models and multilinguality to utilize these linguistic similarities. The second part of the book explores methods for machine transliteration involving related languages based on multilingual and unsupervised approaches. Through extensive experiments over a wide variety of languages, the efficacy of these methods is established. Features Novel methods for machine translation and transliteration between related languages, supported with experiments on a wide variety of languages. An overview of past literature on machine translation for related languages. A case study about machine translation for related languages between 10 major languages from India, which is one of the most linguistically diverse country in the world. The book presents important concepts and methods for machine translation involving related languages. In general, it serves as a good reference to NLP for related languages. It is intended for students, researchers and professionals interested in Machine Translation, Translation Studies, Multilingual Computing Machine and Natural Language Processing. It can be used as reference reading for courses in NLP and machine translation. Anoop Kunchukuttan is a Senior Applied Researcher at Microsoft India. His research spans various areas on multilingual and low-resource NLP. Pushpak Bhattacharyya is a Professor at the Department of Computer Science, IIT Bombay. His research areas are Natural Language Processing, Machine Learning and AI (NLP-ML-AI). Prof. Bhattacharyya has published more than 350 research papers in various areas of NLP.

Download or read book Machine Learning in Translation Corpora Processing written by Krzysztof Wolk and published by CRC Press. This book was released on 2019-02-25 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews ways to improve statistical machine speech translation between Polish and English. Research has been conducted mostly on dictionary-based, rule-based, and syntax-based, machine translation techniques. Most popular methodologies and tools are not well-suited for the Polish language and therefore require adaptation, and language resources are lacking in parallel and monolingual data. The main objective of this volume to develop an automatic and robust Polish-to-English translation system to meet specific translation requirements and to develop bilingual textual resources by mining comparable corpora.

Download or read book Joint Prediction of Word Alignment and Alignment Types for Statistical Machine Translation written by Te Bu and published by . This book was released on 2015 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning word alignments between parallel sentence pairs is an important task in Statistical Machine Translation. Existing models for word alignment have assumed that word alignment links are untyped. In this work, we propose new machine learning models that use linguistically informed link types to enrich word alignments. We use 11 different alignment link types based on annotated data released by the Linguistics Data Consortium. We first provide a solution to the sub-problem of alignment type prediction given an aligned word pair and then propose two different models to simultaneously predict word alignment and alignment types. Our experimental results show that we can recover alignment link types with an F-score of 81.4%. Our joint model improves the word alignment F-score by 4.6% over a baseline that does not use typed alignment links. We expect typed word alignments to benefit SMT and other NLP tasks that rely on word alignments.

Download or read book Statistical Machine Translation written by Franz Josef Och and published by . This book was released on 2002 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Constrained Word Alignment Models for Statistical Machine Translation written by Ma Yanjun and published by . This book was released on 2009 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Gaussian Alignments in Statistical Translation Models written by Ali H. Mohammad and published by . This book was released on 2006 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine translation software has been under development almost since the birth of the electronic computer. Current state-of-the-art methods use statistical techniques to learn how to translate from one natural language to another from a corpus of hand-translated text. The success of these techniques comes from two factors: a simple statistical model and vast training data sets. The standard agenda for improving such models is to enable it to model greater complexity; however, it is a byword within the machine learning community that added complexity must be supported with more training data. Given that current models already require huge amounts of data, our agenda is instead to simplify current models before adding extensions. We present one such simplification, which results in fewer than 10% as many alignment model parameters and produces results competitive with the original model. An unexpected benefit of this technique is that it naturally gives a measure for how difficult it is to translate from one language to another given a data set. Next, we present one suggestion for adding complexity to model new behavior.

Download or read book A Tree to tree Model for Statistical Machine Translation written by Brooke Alissa Cowan and published by . This book was released on 2008 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we take a statistical tree-to-tree approach to solving the problem of machine translation (MT). In a statistical tree-to-tree approach, first the source-language input is parsed into a syntactic tree structure; then the source-language tree is mapped to a target-language tree. This kind of approach has several advantages. For one, parsing the input generates valuable information about its meaning. In addition, the mapping from a source-language tree to a target-language tree offers a mechanism for preserving the meaning of the input. Finally, producing a target-language tree helps to ensure the grammaticality of the output. A main focus of this thesis is to develop a statistical tree-to-tree mapping algorithm. Our solution involves a novel representation called an aligned extended projection, or AEP. The AEP, inspired by ideas in linguistic theory related to tree-adjoining grammars, is a parse-tree like structure that models clause-level phenomena such as verbal argument structure and lexical word-order. The AEP also contains alignment information that links the source-language input to the target-language output. Instead of learning a mapping from a source-language tree to a target-language tree, the AEP-based approach learns a mapping from a source-language tree to a target-language AEP. The AEP is a complex structure, and learning a mapping from parse trees to AEPs presents a challenging machine learning problem. In this thesis, we use a linear structured prediction model to solve this learning problem. A human evaluation of the AEP-based translation approach in a German-to-English task shows significant improvements in the grammaticality of translations. This thesis also presents a statistical parser for Spanish that could be used as part of a Spanish/English translation system.