3,431 research outputs found
Learning Fault-tolerant Speech Parsing with SCREEN
This paper describes a new approach and a system SCREEN for fault-tolerant
speech parsing. SCREEEN stands for Symbolic Connectionist Robust EnterprisE for
Natural language. Speech parsing describes the syntactic and semantic analysis
of spontaneous spoken language. The general approach is based on incremental
immediate flat analysis, learning of syntactic and semantic speech parsing,
parallel integration of current hypotheses, and the consideration of various
forms of speech related errors. The goal for this approach is to explore the
parallel interactions between various knowledge sources for learning
incremental fault-tolerant speech parsing. This approach is examined in a
system SCREEN using various hybrid connectionist techniques. Hybrid
connectionist techniques are examined because of their promising properties of
inherent fault tolerance, learning, gradedness and parallel constraint
integration. The input for SCREEN is hypotheses about recognized words of a
spoken utterance potentially analyzed by a speech system, the output is
hypotheses about the flat syntactic and semantic analysis of the utterance. In
this paper we focus on the general approach, the overall architecture, and
examples for learning flat syntactic speech parsing. Different from most other
speech language architectures SCREEN emphasizes an interactive rather than an
autonomous position, learning rather than encoding, flat analysis rather than
in-depth analysis, and fault-tolerant processing of phonetic, syntactic and
semantic knowledge.Comment: 6 pages, postscript, compressed, uuencoded to appear in Proceedings
of AAAI 9
Robust Parsing of Spoken Dialogue Using Contextual Knowledge and Recognition Probabilities
In this paper we describe the linguistic processor of a spoken dialogue
system. The parser receives a word graph from the recognition module as its
input. Its task is to find the best path through the graph. If no complete
solution can be found, a robust mechanism for selecting multiple partial
results is applied. We show how the information content rate of the results can
be improved if the selection is based on an integrated quality score combining
word recognition scores and context-dependent semantic predictions. Results of
parsing word graphs with and without predictions are reported.Comment: 4 pages, LaTex source, 3 PostScript figures, uses epsf.sty and
ETRW.sty, to appear in Proceedings of ESCA Workshop on Spoken Dialogue
Systems, Denmark, May 30-June
Evaluation of the NLP Components of the OVIS2 Spoken Dialogue System
The NWO Priority Programme Language and Speech Technology is a 5-year
research programme aiming at the development of spoken language information
systems. In the Programme, two alternative natural language processing (NLP)
modules are developed in parallel: a grammar-based (conventional, rule-based)
module and a data-oriented (memory-based, stochastic, DOP) module. In order to
compare the NLP modules, a formal evaluation has been carried out three years
after the start of the Programme. This paper describes the evaluation procedure
and the evaluation results. The grammar-based component performs much better
than the data-oriented one in this comparison.Comment: Proceedings of CLIN 9
The Speech-Language Interface in the Spoken Language Translator
The Spoken Language Translator is a prototype for practically useful systems
capable of translating continuous spoken language within restricted domains.
The prototype system translates air travel (ATIS) queries from spoken English
to spoken Swedish and to French. It is constructed, with as few modifications
as possible, from existing pieces of speech and language processing software.
The speech recognizer and language understander are connected by a fairly
conventional pipelined N-best interface. This paper focuses on the ways in
which the language processor makes intelligent use of the sentence hypotheses
delivered by the recognizer. These ways include (1) producing modified
hypotheses to reflect the possible presence of repairs in the uttered word
sequence; (2) fast parsing with a version of the grammar automatically
specialized to the more frequent constructions in the training corpus; and (3)
allowing syntactic and semantic factors to interact with acoustic ones in the
choice of a meaning structure for translation, so that the acoustically
preferred hypothesis is not always selected even if it is within linguistic
coverage.Comment: 9 pages, LaTeX. Published: Proceedings of TWLT-8, December 199
Learning dialog act processing
In this paper we describe a new approach for learning dialog act processing. In this approach we integrate a symbolic semantic segmentation parser with a learning dialog act network. In order to support the unforeseeable errors and variations of spoken language we have concentrated on robust data-driven learning. This approach already compares favorably
with the statistical average plausibility method, produces a segmentation and dialog act assignment for all utterances in a robust manner, and reduces knowledge engineering since it can be bootstrapped from rather small corpora. Therefore, we consider this new approach as very promising for learning dialog act processing
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