SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis
  Using Artificial Neural Networks
  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
flat 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 flat 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 have developed the
SCREEN system which is based on this new robust, learned and flat analysis. In
this paper, we focus on a detailed description of SCREEN's architecture, the
flat syntactic and semantic analysis, the interaction with a speech recognizer,
and a detailed evaluation analysis of the robustness under the influence of
noisy or incomplete input. The main result of this paper is that flat
representations allow more robust processing of spontaneous spoken language
than deeply structured representations. In particular, we show how the
fault-tolerance and learning capability of connectionist networks can support a
flat analysis for providing more robust spoken-language processing within an
overall hybrid symbolic/connectionist framework.
