2,028 research outputs found

    Towards an Automatic Dictation System for Translators: the TransTalk Project

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    Professional translators often dictate their translations orally and have them typed afterwards. The TransTalk project aims at automating the second part of this process. Its originality as a dictation system lies in the fact that both the acoustic signal produced by the translator and the source text under translation are made available to the system. Probable translations of the source text can be predicted and these predictions used to help the speech recognition system in its lexical choices. We present the results of the first prototype, which show a marked improvement in the performance of the speech recognition task when translation predictions are taken into account.Comment: Published in proceedings of the International Conference on Spoken Language Processing (ICSLP) 94. 4 pages, uuencoded compressed latex source with 4 postscript figure

    Speech Synthesis Based on Hidden Markov Models

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    JTEC panel report on machine translation in Japan

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    The goal of this report is to provide an overview of the state of the art of machine translation (MT) in Japan and to provide a comparison between Japanese and Western technology in this area. The term 'machine translation' as used here, includes both the science and technology required for automating the translation of text from one human language to another. Machine translation is viewed in Japan as an important strategic technology that is expected to play a key role in Japan's increasing participation in the world economy. MT is seen in Japan as important both for assimilating information into Japanese as well as for disseminating Japanese information throughout the world. Most of the MT systems now available in Japan are transfer-based systems. The majority of them exploit a case-frame representation of the source text as the basis of the transfer process. There is a gradual movement toward the use of deeper semantic representations, and some groups are beginning to look at interlingua-based systems

    Towards Machine Speech-to-speech Translation

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    There has been a good deal of research on machine speech-to-speech translation (S2ST) in Japan, and this article presents these and our own recent research on automatic simultaneous speech translation. The S2ST system is basically composed of three modules: large vocabulary continuous automatic speech recognition (ASR), machine text-to-text translation (MT) and text-to-speech synthesis (TTS). All these modules need to be multilingual in nature and thus require multilingual speech and corpora for training models. S2ST performance is drastically improved by deep learning and large training corpora, but many issues still still remain such as simultaneity, paralinguistics, context and situation dependency, intention and cultural dependency. This article presents current on-going research and discusses issues with a view to next-generation speech-to-speech translation.En Japón se han llevado a cabo muchas actividades de investigación acerca de la traducción automática del habla. Este artículo pretende ofrecer una visión general de dichas actividades y presentar las que se han realizado más recientemente. El sistema S2ST está formado básicamente por tres módulos: el reconocimiento automático del habla continua y de amplios vocabularios (Automatic Speech Recognition, ASR), la traducción automática de textos (Machine translation, MT) y la conversión de texto a voz (Text-to-Speech Synthesis, TTS). Todos los módulos deben ser plurilingües, por lo cual se requieren discursos y corpus multilingües para los modelos de formación. El rendimiento del sistema S2ST mejora considerablemente por medio de un aprendizaje profundo y grandes corpus formativos. Sin embargo, todavía hace falta tratar diversos aspectos, com la simultaneidad, la paralingüística, la dependencia del contexto y de la situación, la intención y la dependencia cultural. Por todo ello, repasaremos las actividades de investigación actuales y discutiremos varias cuestiones relacionadas con la traducción automática del habla de última generación.Al Japó s'han dut a terme moltes activitats de recerca sobre la traducció automàtica de la parla. Aquest article n'ofereix una visió general i presenta les activitats que s'han efectuat més recentment. El sistema S2ST es compon bàsicament de tres mòduls: el reconeixement automàtic de la parla contínua i de vocabularis extensos (Automatic Speech Recognition, ASR), la traducció automàtica de textos (Machine translation, MT) i la conversió de text a veu (Text-to-Speech Synthesis, TTS). Tots els mòduls han de ser plurilingües, per la qual cosa es requereixen discursos i corpus multilingües per als models de formació. El rendiment del sistema S2ST millora considerablement per mitjà d'un aprenentatge profund i de grans corpus formatius. Tanmateix, encara cal tractar diversos aspectes, com la simultaneïtat, la paralingüística, la dependència del context i de la situació, la intenció i la dependència cultural. Així, farem un repàs a les activitats de recerca actuals i discutirem diverses qüestions relacionades amb la traducció automàtica de la parla d'última generació

    In no uncertain terms : a dataset for monolingual and multilingual automatic term extraction from comparable corpora

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    Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in low inter-annotator agreement. There is a large need for well-documented, manually validated datasets, especially in the rising field of multilingual term extraction from comparable corpora, which presents a unique new set of challenges. In this paper, a new approach is presented for both monolingual and multilingual term annotation in comparable corpora. The detailed guidelines with different term labels, the domain- and language-independent methodology and the large volumes annotated in three different languages and four different domains make this a rich resource. The resulting datasets are not just suited for evaluation purposes but can also serve as a general source of information about terms and even as training data for supervised methods. Moreover, the gold standard for multilingual term extraction from comparable corpora contains information about term variants and translation equivalents, which allows an in-depth, nuanced evaluation

    Validating multilingual hybrid automatic term extraction for search engine optimisation : the use case of EBM-GUIDELINES

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    Tools that automatically extract terms and their equivalents in other languages from parallel corpora can contribute to multilingual professional communication in more than one way. By means of a use case with data from a medical web site with point of care evidence summaries (Ebpracticenet), we illustrate how hybrid multilingual automatic term extraction from parallel corpora works and how it can be used in a practical application such as search engine optimisation. The original aim was to use the result of the extraction to improve the recall of a search engine by allowing automated multilingual searches. Two additional possible applications were found while considering the data: searching via related forms and searching via strongly semantically related words. The second stage of this research was to find the most suitable format for the required manual validation of the raw extraction results and to compare the validation process when performed by a domain expert versus a terminologist
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