Learning complex output representations in connectionist parsing of spoken language

Buo, F.D.; Polzin, Thomas; Waibel, Alex

doi:10.1109/icassp.1994.389280

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…Recent further extensions based on parsec provide more structure and use annotated linguistic features (Bu et al, 1994). The authors state that they \implemented (based on parsec) a connectionist system" which should approximate a shift reduce parser.…”

Section: Comparisons With Related Hybrid Systemsmentioning

confidence: 99%

“…Comparing parsec and screen, parsec aims more at supporting symbolic rules by using symbolic transformations (triggered by connectionist networks) and by integrating linguistic features. Currently, the linguistic features in the recent parsec extension (Bu et al, 1994) provide more structural and morphological knowledge than screen does. Therefore, currently it appears to be easier to integrate the parsec extension into larger systems of high level linguistic processing.…”

Section: Comparisons With Related Hybrid Systemsmentioning

confidence: 99%

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SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

Wermter

Weber

1997

jair

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Previous approaches of analyzing spontaneously spoken language often have been based on encoding syntactic and semantic knowledge manually and symbolically. While there has been some progress using statistical or connectionist language models, many current spoken-language systems still use a relatively brittle, hand-coded symbolic grammar or symbolic semantic component.In contrast, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a at analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a at connectionist analysis. This screening approach aims at supporting speech and language processing by using 1 data-driven learning and 2 robustness of connectionist networks. In order to test this approach, we h a ve developed the screen system which i s based on this new robust, learned and at analysis.In this paper, we focus on a detailed description of screen's architecture, the at syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the in uence of noisy or incomplete input. The main result of this paper is that at representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show h o w the fault-tolerance and learning capability of connectionist networks can support a at analysis for providing more robust spoken-language processing within an overall hybrid symbolic connectionist framework.

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Section: Comparisons With Related Hybrid Systemsmentioning

confidence: 99%

Section: Comparisons With Related Hybrid Systemsmentioning

confidence: 99%

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

Wermter

Weber

1997

jair

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“…The most relevant is that of Buo et al 1994 who report a similar approach to parsing as the one taken here. They use one network to find phrase boundaries and one to label the phrase.…”

Section: Comparisons With Previous Workmentioning

confidence: 99%

“…An alternative and more successful approach is to tightly couple connectionist and symbolic modules so that they work together (Wermter and Weber 1994, Buo et al 1994, Buo and Waibel 1996, Henderson and Lane 1998. With the exception of Buo et al 1994 the connectionist modules in these parsers were also trained using naturally occurring corpus data.…”

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confidence: 99%

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A corpus-based connectionist architecture for large-scale natural language parsing

Tepper¹,

Powell²,

Palmer-Brown³

2002

Connection Science

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Abstract. We describe a deterministic shift-reduce parsing model that combines the advantages of connectionism with those of traditional symbolic models for parsing realistic sub-domains of natural language. It is a modular system that learns to annotate natural language texts with syntactic structure.The parser acquires its linguistic knowledge directly from pre-parsed sentence examples extracted from an annotated corpus. The connectionist modules enable the automatic learning of linguistic constraints and provide a distributed representation of linguistic information that exhibits tolerance to grammatical variation. The inputs and outputs of the connectionist modules represent symbolic information which can be easily manipulated and interpreted and provide the basis for organising the parse. Performance is evaluated using labelled precision and recall. (For a test set of 4,128 words, precision and recall of 75% and 69% respectively were achieved). The work presented represents a significant step towards demonstrating that broad coverage parsing of natural language can be achieved with simple hybrid connectionist architectures which approximate shift-reduce parsing behaviours. Crucially, the model is adaptable to the grammatical framework of the training corpus used and so is not predisposed to a particular grammatical formalism.

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JANUS-II-translation of spontaneous conversational speech

Waibel

Finke

Gates

et al.

1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings

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Learning complex output representations in connectionist parsing of spoken language

Abstract: Due t o r o b u s t n e s s , l e a r n a b ilitya n d e a s e o f i n t e g r a t iono f e r e n t i n f o r m a t ionsources, c o n n e c t ionist p a r s ings y s t e m s r p a r s ing s p o k e n l a n g u a g e ,

Cited by 4 publications

References 6 publications

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

A corpus-based connectionist architecture for large-scale natural language parsing

JANUS-II-translation of spontaneous conversational speech

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