Hierarchical feature-based translation for scalable natural language understanding

Hierarchical feature-based translation for scalable natural language understanding
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基于分层特征的翻译，用于可扩展的自然语言理解

DOI：
10.21437/icslp.2000-583
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发表时间：
2000
期刊：
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影响因子：
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通讯作者：
Jan Kleindienst
Jan Kleindienst
中科院分区：
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文献类型：
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作者：
G. Ramaswamy;Jan Kleindienst

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对于具有大量统计上易混淆但语义上不同的正式命令的复杂自然语言理解系统，在单个步骤中执行用户输入到正式命令的准确率翻译存在许多困难。本文讨论了自然语言理解中的可扩展性问题，并描述了一种以分层方式执行翻译的方法。分层方法提高了系统准确性，降低了翻译的计算复杂度，在训练和解码期间提供了额外的数值鲁棒性，并允许更有效地打包自然语言理解系统的组件。
For complex natural language understanding systems with a large number of statistically confusable but semantically different formal commands, there are many difficulties in performing an accu-rate translation of a user input into a formal command in a single step. This paper addresses scalability issues in natural language understanding, and describes a method for performing the translation in a hierarchical manner. The hierarchical method improves the system accuracy, reduces the computational complexity of the translation, provides additional numerical robustness during training and decoding, and permits a more efficient packaging of the components of the natural language understanding system.