The Candide system for machine translation

Berger, Adam; Brown, Peter F.; Pietra, Stephen A. Della; Pietra, Vincent J. Della; Gillett, John; Lafferty, John; Mercer, R.L.; Printz, Harry; Ureš, Luboš

doi:10.3115/1075812.1075844

Cited by 87 publications

(58 citation statements)

References 7 publications

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“…Whereas stochastic modelling is widely used in speech recognition, there are so far only a few research groups that apply stochastic modelling to language translation (Berger et al 1994;Brown et al 1993;Knight 1999). The presentation here is based on work carried out in the framework of the EUTRANS project (Casacuberta et al 2001) and the VERBMOBIL project (Wahlster 2000).…”

Section: Resultsmentioning

confidence: 99%

Stochastic Modelling: From Pattern Classification to Speech Recognition and Language Translation

Ney

1999

Informatik Aktuell

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This paper gives an overview of the stochastic modelling approach to machine translation.Starting with the Bayes decision rule as in pattern classification and speech recognition, we show how the resulting system architecture can be structured into three parts: the language model probability, the string translation model probability and the search procedure that generates the word sequence in the target language. We discuss the properties of the system components and report results on the translation of spoken dialogues in the VERBMOBIL project. The experience obtained in the VERB-MOBIL project, in particular a largescale end-to-end evaluation, showed that the stochastic modelling approach resulted in significantly lower error rates than three competing translation approaches: the sentence error rate was 29% in comparison with 52% to 62% for the other translation approaches.

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Section: Resultsmentioning

confidence: 99%

Stochastic Modelling: From Pattern Classification to Speech Recognition and Language Translation

Ney

1999

Informatik Aktuell

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“…They have been also successfully used in part of speech tagging [4], machine translation [1,3] , information retrieval [2,18], transliteration [11] and text summarization [12]. The reader can refer to [13] for a detailed description of statistical translation models in various applications.…”

Section: Statistical Translation Modelsmentioning

confidence: 99%

“…Brown et al [3] lay out the mathematical foundation of statistical machine translation, while Berger et al [1] presents an overview of Candide, a system that uses probabilistic methods to automatically translate French text into English.…”

Section: Related Workmentioning

confidence: 99%

“…The alignment is hidden because the system can only see a pair of French and English sentences that are translations of one another and has no information about the particular alignment. Obviously, all possible alignments of two given strings are not equally likely, however it is practically more accurate to think about the translation process in such a way (see [1,3] for more details) and then employ parameter estimation techniques to determine the probabilities of applying given operators on particular input strings.…”

Section: Statistical Translation Modelsmentioning

confidence: 99%

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Query Modulation For Web-Based Question Answering

Radev

Qi²,

Zheng

et al. 2008

Advances in Open Domain Question Answering

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Abstract:The web is now becoming one of the largest information and knowledge repositories. Many large scale search engines (Google, Fast, Northern Light, etc.) have emerged to help users find information. In this paper, we study how we can effectively use these existing search engines to mine the Web and discover the -correct'' answers to factual natural language questions. We propose a probabilistic algorithm called QASM (Question Answering using Statistical Models) that learns the best query paraphrase of a natural language question. We validate our approach for both local and web search engines using questions from the TREC evaluation.

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“…Due to Bayes theorem (which is defined by (2)) and since the denominator here is independent of e, finding ê is the same as finding e. So, to make the product P(e)P(f |e) as large as possible, equation (3) is presented [6,8,9].…”

Section: Basic Theory Of Statistical Machine Translationmentioning

confidence: 99%

A new model for persian multi-part words edition based on statistical machine translation

Zahedi

Arjomandzadeh

2016

JAIDM

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Multi-part words in English language are hyphenated and hyphen is used to separate different parts. Persian language consists of multi-part words as well. Based on Persian morphology, half-space character is needed to separate parts of multi-part words where in many cases people incorrectly use space character instead of half-space character. This common incorrectly use of space leads to some serious issues in Persian text processing and text readability. In order to cope with the issues, this work proposes a new model to correct spacing in multi-part words. The proposed method is based on statistical machine translation paradigm. In machine translation paradigm, text in source language is translated into a text in destination language on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The proposed method uses statistical machine translation techniques considering unedited multi-part words as a source language and the space-edited multi-part words as a destination language. The results show that the proposed method can edit and improve spacing correction process of Persian multi-part words with a statistically significant accuracy rate.

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The Candide system for machine translation

Cited by 87 publications

References 7 publications

Stochastic Modelling: From Pattern Classification to Speech Recognition and Language Translation

Stochastic Modelling: From Pattern Classification to Speech Recognition and Language Translation

Query Modulation For Web-Based Question Answering

A new model for persian multi-part words edition based on statistical machine translation

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