口语理解的联合生成和判别模型

Marco Dinarelli, Alessandro Moschitti, G. Riccardi
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引用次数: 3

摘要

口语理解的目的是将自然语言的口语句子映射成语义表示。在过去十年中,主要采用了两种方法:生成模型和判别模型。前者对过拟合的鲁棒性更强,而后者对许多不相关的特征的鲁棒性更强。此外,这些方法编码先验知识的方式也各不相同,它们的相对性能也会根据任务的不同而变化。在本文中,我们描述了一个训练框架,其中使用了这两个模型:生成模型产生一个排序假设列表,而判别模型,依赖于字符串核和支持向量机,重新排序这样的列表。我们在欧洲LUNA项目生产的新语料库上测试了这种方法。结果表明,在最先进的概念分割和标记方面有了很大的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint generative and discriminative models for spoken language understanding
Spoken Language Understanding aims at mapping a natural language spoken sentence into a semantic representation. In the last decade two main approaches have been pursued: generative and discriminative models. The former is more robust to overfitting whereas the latter is more robust to many irrelevant features. Additionally, the way in which these approaches encode prior knowledge is very different and their relative performance changes based on the task. In this paper we describe a training framework where both models are used: a generative model produces a list of ranked hypotheses whereas a discriminative model, depending on string kernels and Support Vector Machines, re-ranks such list. We tested such approach on a new corpus produced in the European LUNA project. The results show a large improvement on the state-of-the-art in concept segmentation and labeling.
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