Dialogos: a robust system for human-machine spoken dialogue on the telephone

Albesano, D.; Baggia, Paolo; Danieli, Morena; Gemello, Roberto; Gerbino, Elisabetta; Rullent, Claudio

doi:10.1109/icassp.1997.596145

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…By the end of the Nineties some vocal services have been activated, especially in Europe, in which vocal input is interpreted by a signal processor and a parser, while the answer, also in vocal form, is planned by a dialogue manager module according to a number of contextual parameters. There are some systems that can be considered a sort of milestones, such as SUNDIAL (Peckham 1993), Dialogos (Albesano et al 1997), or the DUKE University system (Bierman et al 1993).…”

Section: Spoken Dialogue Systemsmentioning

confidence: 99%

State of the art in Computational Linguistics

Ferrari¹

2004

Linguistics Today – Facing a Greater Challenge

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Computational Linguistics has a long history, dating back to the Fifties, during which it developed a whole set of computational models and implementations, theories, methodologies and applications. It is difficult to give a sensible account of its present state without going back a little to the main steps through which this discipline evolved towards its present state. Since its origins, Computational Linguistics has been in an intermediate position between Computer Science and Artificial Intelligence, Linguistics and Cognitive Science, and Engineering. Computer Science itself shares its roots with Computational Linguistics; parsing, which is central for the design of compilers for programming languages (Aho and Ullmann 1977: 6), is also the building block of any natural language processing engine, and both are the realizations of the chomskian theory of formal languages (Chomsky 1957). The same theory, together with the corresponding computational model, has given a contribution to the general hypothesis of Artificial Intelligence, that human behaviours usually judged intelligent could be simulated in a computer in a principled way. Oversimplifying, Artificial Intelligence aims at modelling a number of behaviours through three very general paradigms, theorem proving, problem solving and planning, and language understanding and production. The history of both disciplines is rich in intersections, especially between language processing and planning, as in SHRDLU (Winograd 1971) or, more recently, in ARGOT (Allen et al. 1982, Allen 1983), with all its practical and theoretical follow-ups; modern dialogue systems in all their forms and applications are derived from the dialogue model J.Allen designed for ARGOT. The commitment to "simulation of behaviour", shared by Artificial Intelligence and and a relevant part of Computational Linguistics, makes them also share the effort for "cognitive modelling" of different human behaviours, including the use of language. This is probably one of the reasons why Linguistics appears in the set of sciences originally interested in the arising of the new discipline called Cognitive Science (www.cognitivesciencesociety.org). Since the Seventies, when language technology reached a state of maturity such as to allow the realization of some applications, Engineering has been interested in some of the language processing techniques, and it appeared soon that the approach introduced by engineers was certainly less theoretically and cognitively interesting, but more effective in many ways. By now, we can say that while Computational Linguists were, and are, more interested in the correctness and plausibility of their models, Engineers were, and are, more interested in the usability of tools and techniques, even

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Section: Spoken Dialogue Systemsmentioning

confidence: 99%

State of the art in Computational Linguistics

Ferrari¹

2004

Linguistics Today – Facing a Greater Challenge

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“…In (Albesano et al, 1997), evaluation of Dialogos is described. A special metric is used for transaction success.…”

Section: Metrical Evaluationmentioning

confidence: 99%

“…PNT Pointing, including menus, tables, buttons, maps, drag-n-drop VIR 3D, virtual reality MUL Support for multiple participants System, name and reference (Lie et al, 1998) theatre booking x x x x Dialogos: (Albesano et al, 1997) railway enquiry x x x TRAINS93: (Sikorski and Allen, 1996), (Stent and Allen, 1997) route planning x x x ARTIMIS: ) telephone enquiry x x x PADIS: (Bouwman, 1998) telephone enquiry x x x COSMA: (Busemann et al, 1997) appointment scheduling x x x CASSY: (Gerlach and Onken, 1993) pilot assistance x x x WOMBAT: (Blandford, 1995) purchase planning x x CARTOON: (Martin, 1997) map enquiry x x x InterLACE: (Trafton et al, 1997) map enquiry x x x ORIMUHS 1 (Encarnacão, 1997) intelligent help x x x x QuickSet (Johnston et al, 1997) war tactics x x x x x Circuit fix-it shop (Smith and Hipp, 1994) circuit repair x x x Schisma ( , more specific information about the parser can be found in (Lie et al, 1998) ) is a theatre inquiry and booking system. Its language parser is based on 'rewrite-and-understand', which means any language utterance is rewritten, using production rules, into a 'canonical' form.…”

Section: Strategies and Stylesmentioning

confidence: 99%

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Overview of Current Research

López-de-Silanes¹

2007

Social Skills and Autistic Spectrum Disorders

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This text is an overview of literature relevant to the development and modelling of interactive systems. It is the most comprehensive part of the documentation I made during the literature research stage of my PhD project, which should result in a modelling framework for interactive systems. More precisely, the scope of this project is natural language, multimodal, and virtual reality systems, and ways of modelling the dynamics of interaction of such systems for the purpose of aiding system development. The topics addressed in this text naturally include existing systems and modelling techniques, but theoretical frameworks and methodological issues are also addressed. Short introductions are given to cognitive psychology, semiotics, communication models, and HCI. Systems and strategies are reviewed, covering the topics multimodality, redundancy and confirmation, and ways of modelling dialogue and task structure. Methodological issues are reviewed, in particular some methodologies, and strategies for design/implementation and analysis/evaluation. Finally, some conclusions are given, as well as an indication of the directions of my future research.1

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“…A part of this corpus collected from 493 naive users (1,363 dialogues, 13,123 utterances) is reported in[12], where the evaluation results of the system are given.…”

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confidence: 99%

Specialized language models using dialogue predictions

Popovici

Baggia

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing

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This paper analyses language modeling in spoken dialogue systems for accessing a database. The use of several language models obtained by exploiting dialogue predictions gives better results than the use of a single model for the whole dialogue interaction. For this reason several models have been created, each one for a specific system question, such as the request or the confirmation of a parameter.The use of dialogue-dependent language models increases the performance both at the recognition and at the understanding level, especially on answers to system requests. Moreover using other methods to increase performances, like automatic clustering of vocabularywords or the use of better acoustic models during recognition, does not affect the improvements given by dialogue-dependent language models.The system used in our experiments is Dialogos, the Italian spoken dialogue system used for accessing railway timetable information over the telephone. The experiments were carried out on a large corpus of dialogues collected using Dialogos.

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Dialogos: a robust system for human-machine spoken dialogue on the telephone

Cited by 7 publications

References 9 publications

State of the art in Computational Linguistics

State of the art in Computational Linguistics

Overview of Current Research

Specialized language models using dialogue predictions

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