Functional Validation of a Machine Interpretation System: Verbmobil

Tessiore, Lorenzo; Hahn, Walther von

doi:10.1007/978-3-662-04230-4_45

Cited by 9 publications

(7 citation statements)

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“…This endto-end evaluation of the VERBMOBIL system was performed at the University of Hamburg (Tessiore et al 2000). In each session of this evaluation, two native speakers conducted a dialogue.…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…Whereas during the progress of the project many offline tests were carried out for the optimization and tuning of the statistical approach, the most important evaluation was the final evaluation of the VERBMOBIL prototype in spring 2000. This endto-end evaluation of the VERBMOBIL system was performed at the University of Hamburg (Tessiore et al 2000). In each session of this evaluation, two native speakers conducted a dialogue.…”

Section: Resultsmentioning

confidence: 99%

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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%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

Ney

1999

Informatik Aktuell

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“…2.2 and [15]) and the AT approach (see Table 4. In [2,13] an example-based approach is mentioned that is to some extend similar to PBT. The results are not included because they are evaluated on a different test set and therefore not comparable.…”

Section: Comparison With Other Systemsmentioning

confidence: 98%

“…In the final project evaluation [13], several approaches were evaluated on the same test data. In addition to a classical rule-based approach [4] and a dialogueact based approach [12] there were three data-driven approaches, namely an example-based [1], a substring-based [2] and a statistical approach developed in the authors' group.…”

Section: Introductionmentioning

confidence: 99%

Phrase-Based Statistical Machine Translation

Zens

Och

Ney

2002

KI 2002: Advances in Artificial Intelligence

224

213

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Abstract. This paper is based on the work carried out in the framework of the Verbmobil project, which is a limited-domain speech translation task (German-English). In the final evaluation, the statistical approach was found to perform best among five competing approaches. In this paper, we will further investigate the used statistical translation models. A shortcoming of the single-word based model is that it does not take contextual information into account for the translation decisions. We will present a translation model that is based on bilingual phrases to explicitly model the local context. We will show that this model performs better than the single-word based model. We will compare monotone and non-monotone search for this model and we will investigate the benefit of using the sum criterion instead of the maximum approximation.

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Mobile Speech-to-Speech Translation of Spontaneous Dialogs: An Overview of the Final Verbmobil System

Wahlster

2000

Artificial Intelligence

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Abstract. Verbmobil is a speaker-independent and bidirectional speech-to-speech translation system for spontaneous dialogs in mobile situations. It recognizes spoken input, analyses and translates it, and finally utters the translation. The multilingual system handles dialogs in three business-oriented domains, with context-sensitive translation between three languages (German, English, and Japanese). Since Verbmobil emphasizes the robust processing of spontaneous dialogs, it poses difficult challenges to human language technology, that we discuss in this paper. We present Verbmobil as a hybrid system incorporating both deep and shallow processing schemes. We describe the anatomy of Verbmobil and the functionality of its main components. We discuss Verbmobil's multi-blackboard architecture that is based on packed representations at all processing stages. These packed representations together with formalisms for underspecification capture the non-determinism in each processing phase, so that the remaining uncertainties can be reduced by linguistic, discourse and domain constraints as soon as they become applicable. We present Verbmobil's multi-engine approach, eg. its use of five concurrent translation engines: statistical translation, case-based translation, substring-based translation, dialog-act based translation, and semantic transfer. Distinguishing features like the multilingual prosody module and the generation of dialog summaries are highlighted. We conclude that Verbmobil has successfully met the project goals with more than 80% of approximately correct translations and a 90% success rate for dialog tasks.

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Functional Validation of a Machine Interpretation System: Verbmobil

Cited by 9 publications

References 1 publication

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

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

Phrase-Based Statistical Machine Translation

Mobile Speech-to-Speech Translation of Spontaneous Dialogs: An Overview of the Final Verbmobil System

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