Efficient determinization of tagged word lattices using categorial and lexicographic semirings

Izhak Shafran, R. Sproat, M. Yarmohammadi, Brian Roark
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引用次数: 10

Abstract

Speech and language processing systems routinely face the need to apply finite state operations (e.g., POS tagging) on results from intermediate stages (e.g., ASR output) that are naturally represented in a compact lattice form. Currently, such needs are met by converting the lattices into linear sequences (n-best scoring sequences) before and after applying the finite state operations. In this paper, we eliminate the need for this unnecessary conversion by addressing the problem of picking only the single-best scoring output labels for every input sequence. For this purpose, we define a categorial semiring that allows determinzation over strings and incorporate it into a 〈Tropical, Categorial〉 lexicographic semiring. Through examples and empirical evaluations we show how determinization in this lexicographic semiring produces the desired output. The proposed solution is general in nature and can be applied to multi-tape weighted transducers that arise in many applications.
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使用分类和词典半分割的标记词格的有效确定
语音和语言处理系统通常需要将有限状态操作(例如,POS标记)应用于中间阶段(例如,ASR输出)的结果,这些结果自然地以紧凑的晶格形式表示。目前,在应用有限状态运算之前和之后,通过将格转换为线性序列(n-最佳评分序列)来满足这种需求。在本文中,我们通过解决为每个输入序列只选择单最佳得分输出标签的问题,消除了这种不必要的转换的需要。为此,我们定义了一个允许对字符串进行确定的分类半环,并将其合并到< Tropical, categorical >字典半环中。通过示例和经验评估,我们展示了词典半循环中的确定如何产生期望的输出。所提出的解决方案本质上是通用的,可以应用于许多应用中出现的多带加权传感器。
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