From rule-based to statistical grammars: Continuous improvement of large-scale spoken dialog systems

David, Sina; Evanini, Keelan; Liscombe, Jackson; Hunter, Peter; Dayanidhi, Krishna; Pieraccini, Roberto

doi:10.1109/icassp.2009.4960683

Cited by 31 publications

(12 citation statements)

References 6 publications

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“…Recently several works showed that the use of automatic machine translation at different levels of the understanding process can be useful to port a SLU system with a minimum human effort [1,2,3,4,5]. If good performance can already be obtained, a difficulty remains in the capacity of these approaches to transfer all the semantic knowledge from the source data to the target language.…”

Section: Introductionmentioning

confidence: 99%

Combination of stochastic understanding and machine translation systems for language portability of dialogue systems

Jabaian

Besacier

Lefèvre

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, several approaches for language portability of dialogue systems are investigated with a focus on the spoken language understanding (SLU) component. We show that the use of statistical machine translation (SMT) can greatly reduce the time and cost of porting an existing system from a source to a target language. Using automatically translated training data we study phrase-based machine translation as an alternative to conditional random fields for conceptual decoding to compensate for the loss of a precise concept-word alignment. Also two ways to increase SLU robustness to translation errors (smeared training data and translation postediting) are shown to improve performance when test data are translated then decoded in the source language. Overall the combination of all these approaches allows to reduce even further the concept error rate. Experiments were carried out on the French MEDIA dialogue corpus with a subset manually translated into Italian.

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Section: Introductionmentioning

confidence: 99%

Combination of stochastic understanding and machine translation systems for language portability of dialogue systems

Jabaian

Besacier

Lefèvre

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…On the other hand, artificial intelligent based chatbots can better understand student intents. Even the most simple non-rule-based natural language understanding methods significantly outperform the most carefully crafted rule-based systems [26]. The reason is that they can achieve a more profound understanding of the intent and the requested informa-tion, thanks to machine learning techniques [27].…”

Section: Related Workmentioning

confidence: 99%

JAICOB: A Data Science Chatbot

et al. 2020

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The application of natural language to improve students' interaction with information systems is demonstrated to be beneficial. In particular, advances in cognitive computing enable a new way of interaction that accelerates insight from existing information sources, thereby contributing to the process of learning. This work aims at researching the application of cognitive computing in blended learning environments. We propose a modular cognitive agent architecture for pedagogical question answering, featuring social dialogue (small talk), improved for a specific knowledge domain. This system has been implemented as a personal agent to assist students in learning Data Science and Machine Learning techniques. Its implementation includes the training of machine learning models and natural language understanding algorithms in a human-like interface. The effectiveness of the system has been validated through an experiment. INDEX TERMS Cognitive informatics, Educational technology, Human-computer interaction, Machine learning, Natural language processing.

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“…However as new examples are added to the annotated corpus, corpus-based methods can then be applied, replacing or in addition to the initial knowledge-based models. Such a development cycle is described in [5].…”

Section: Related Workmentioning

confidence: 99%

Active learning for rule-based and corpus-based Spoken Language Understanding models

Gotab

Béchet

Damnati

2009

2009 IEEE Workshop on Automatic Speech Recognition &Amp; Understanding

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International audience—Active learning can be used for the maintenance of a deployed Spoken Dialog System (SDS) that evolves with time and when large collection of dialog traces can be collected on a daily basis. At the Spoken Language Understanding (SLU) level this maintenance process is crucial as a deployed SDS evolves quickly when services are added, modified or dropped. Knowledge-based approaches, based on manually written grammars or inference rules, are often preferred as system designers can modify directly the SLU models in order to take into account such a modification in the service, even if no or very little related data has been collected. However as new examples are added to the annotated corpus, corpus-based methods can then be applied, replacing or in addition to the initial knowledge-based models. This paper describes an active learning scheme, based on an SLU criterion, which is used for automatically updating the SLU models of a deployed SDS. Two kind of SLU models are going to be compared: rule-based ones, used in the deployed system and consisting of several thousands of hand-crafted rules; corpus-based ones, based on the automatic learning of classifiers on an annotated corpus

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From rule-based to statistical grammars: Continuous improvement of large-scale spoken dialog systems

Cited by 31 publications

References 6 publications

Combination of stochastic understanding and machine translation systems for language portability of dialogue systems

Combination of stochastic understanding and machine translation systems for language portability of dialogue systems

JAICOB: A Data Science Chatbot

Active learning for rule-based and corpus-based Spoken Language Understanding models

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