SCREEN：使用人工神经网络学习平面句法和语义口语分析
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks
摘要：
在本文中，我们描述了一个所谓的筛选方法学习鲁棒处理的自发口语。一个筛选方法是一个平面分析，它使用浅层序列的类别表示分析的话语在不同的句法，语义和对话水平。我们使用的不是结构深刻的符号分析，而是扁平的联结主义分析。这种筛选方法旨在通过使用（1）数据驱动学习和（2）连接网络的鲁棒性来支持语音和语言处理。为了测试这种方法，我们已经开发了屏幕系统，这是基于这种新的强大的，学习和平面分析。在本文中，我们专注于SCREEN的体系结构的详细描述，平面的语法和语义分析，与语音识别器的互动，并详细评估分析的影响下的噪声或不完整的输入的鲁棒性。本文的主要结果是，平面表示允许更强大的处理自发的口语比深结构化的表示。特别是，我们展示了如何容错和学习能力的连接网络可以支持一个平面的分析，提供更强大的口语处理在一个整体的混合符号/连接框架。
英文摘要：
In this paper, 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.
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