Translation in machine

Xem 1-20 trên 388 kết quả Translation in machine
  • A proper treatment of syntax and semantics in machine translation is introduced and discussed from the empirical viewpoint. For EnglishJapanese machine translation, the syntax directed approach is effective where the Heuristic Parsing Model (HPM) and the Syntactic Role System play important roles. For Japanese-English translation, the semantics directed approach is powerful where the Conceptual Dependency Diagram (CDD) and the Augmented Case Marker System (which is a kind of Semantic Role System) play essential roles.

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  • Problems of machine translation have been investigated in the Soviet Union since 1955.1 A number of groups are carrying out theoretical and experimental work in the area of machine translation. In the Institute of Precision Mechanics and Computer Technology of the Academy of Sciences of the USSR (ITM and VT) dictionaries and codes of rules (algorithms) have been compiled for machine translation from English, Chinese, and Japanese into Russian; and a GermanRussian algorithm is being worked out.

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  • There have been various attempts at using the sublanguage notion for disambiguation and the selection of target language equivalents in machine translation. In this paper a theoretical concept and its implementation in a real MT application are presented. Above this, means of linguistic engineering like weighting mechanisms are proposed.

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  • To translate well, a machine must be furnished with rules that relate meaning to words. These rules may be expressed in terms of probabilities, if they cannot be expressed precisely. Less useful are descriptive rules, particularly those using concepts of psychology.

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  • To give appropriate translation equivalents for target words is one of the most fundamental problems in machine translation systrms. Especially, when the MT systems handle languages that have completely different structures like Japanese and European languages as source and target languages. In this report, we discuss about the data strucutre that enables appropriate selections of translation equivalents for verbs in the target language. This structure is based on the concepts strucutre with associated information relating source and target languages.

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  • The present study is a practical guide to editors who refine partially machine-translated text as a basis for linguistic analysis. The posteditors' tasks are: to code preferred English equivalents, to code English structural symbols, to resolve grammatic properties, and to code syntactic connections (dependencies).

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  • We present a method to transliterate names in the framework of end-to-end statistical machine translation. The system is trained to learn when to transliterate. For Arabic to English MT, we developed and trained a transliterator on a bitext of 7 million sentences and Google’s English terabyte ngrams and achieved better name translation accuracy than 3 out of 4 professional translators. The paper also includes a discussion of challenges in name translation evaluation.

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  • Though most translation systems have some mechanism for translating certain types of divergent predicate-argument structures, they do not provide a genera] procedure that takes advantage of the relationship between lexical-semantic structure and syntactic structure. A divergent predicate-argument structure is one in which the predicate (e.g., the main verb) or its arguments (e.g., the subject and object) do not have the same syntactic ordering properties for both the source and target language. ...

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  • The CAT framework (e.g. Arnold et a]. 1986) was meant to be an anTheoretical research in the area of machine translation usu- swer to the same question, this time for a transfer system, ally involves the search for and creation of an appropriate viz. the Eurotra system. The MiMo formalism is a reformalism. An important issue in this respect is the way in action to the CAT framework and tries to solve several which the compositionality of translation is to be defined. translation problems by formulating an alternative definiIn this paper, we will introduce the anaphoric component tion of...

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  • A formalism is a set of notation with well-defined semantics (namely for the interpretation of the symbols used and their manipulation), by means of which one formally expresses certain domain knowledge, which is to be utilised for specific purposes. In this paper, we are interested in formalisms which are being used or have applications in the domain of machine translation (MT).

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  • In this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memory than is required by current decoder implementations. We detail the computational complexity and average retrieval times for looking up phrase translations in our suffix array-based data structure. We show how sampling can be used to reduce the retrieval time by orders of magnitude with no loss in translation quality. ...

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  • We present a machine learning approach to evaluating the wellformedness of output of a machine translation system, using classifiers that learn to distinguish human reference translations from machine translations. This approach can be used to evaluate an MT system, tracking improvements over time; to aid in the kind of failure analysis that can help guide system development; and to select among alternative output strings. The method presented is fully automated and independent of source language, target language and domain. ...

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  • We propose a real-time machine translation system that allows users to select a news category and to translate the related live news articles from Arabic, Czech, Danish, Farsi, French, German, Italian, Polish, Portuguese, Spanish and Turkish into English.

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  • THE PRINCIPAL differences between the work at The University of Michigan and other work in machine translation is in the emphasis placed on the problem of multiple meaning and the approach to that problem. Our approach consists in translating small groups of words, listing in the dictionary multiple meanings under each word in the group, and finding algorithms which make it possible to choose the proper set of meanings for the group.

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  • The recent work of the Unit has been primarily concerned with the employment of thesauri in machine translation. Limited success has been achieved, in punchedcard tests, in improving the idiomatic quality and so the intelligibility of an initially unsatisfactory translation, by word-for-word procedures, from Italian into English, by using a program which permitted selection of final equivalents from "heads" in Roget's Thesaurus, i.e. lists of synonyms, near-synonyms and associated words and phrases, instead of from previously determined lists of alternative translations....

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  • Research on a non-statistical scheme for the insertion of English articles in machine-translated Russian is described. Ideal article insertion as a goal is challenged as unreasonable. Classification of English nouns, simple syntactic criteria, and multiple printout are the scheme's main features.

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  • Statistical machine translation is often faced with the problem of combining training data from many diverse sources into a single translation model which then has to translate sentences in a new domain. We propose a novel approach, ensemble decoding, which combines a number of translation systems dynamically at the decoding step. In this paper, we evaluate performance on a domain adaptation setting where we translate sentences from the medical domain.

    pdf10p nghetay_1 07-04-2013 34 2   Download

  • We illustrate and explain problems of n-grams-based machine translation (MT) metrics (e.g. BLEU) when applied to morphologically rich languages such as Czech. A novel metric SemPOS based on the deep-syntactic representation of the sentence tackles the issue and retains the performance for translation to English as well.

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  • We present a discriminative learning method to improve the consistency of translations in phrase-based Statistical Machine Translation (SMT) systems. Our method is inspired by Translation Memory (TM) systems which are widely used by human translators in industrial settings.

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  • Recent advances in Machine Translation (MT) have brought forth a new paradigm for building NLP applications in low-resource scenarios. To build a sentiment classifier for a language with no labeled resources, one can translate labeled data from another language, then train a classifier on the translated text. This can be viewed as a domain adaptation problem, where labeled translations and test data have some mismatch.

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