Mixture of mixture n-gram language models

H. Sak, Cyril Allauzen, Kaisuke Nakajima, F. Beaufays
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引用次数: 2

Abstract

This paper presents a language model adaptation technique to build a single static language model from a set of language models each trained on a separate text corpus while aiming to maximize the likelihood of an adaptation data set given as a development set of sentences. The proposed model can be considered as a mixture of mixture language models. The mixture model at the top level is a sentence-level mixture model where each sentence is assumed to be drawn from one of a discrete set of topic or task clusters. After selecting a cluster, each n-gram is assumed to be drawn from one of the given n-gram language models. We estimate cluster mixture weights and n-gram language model mixture weights for each cluster using the expectation-maximization (EM) algorithm to seek the parameter estimates maximizing the likelihood of the development sentences. This mixture of mixture models can be represented efficiently as a static n-gram language model using the previously proposed Bayesian language model interpolation technique. We show a significant improvement with this technique (both perplexity and WER) compared to the standard one level interpolation scheme.
混合n-gram语言模型的混合
本文提出了一种语言模型自适应技术,该技术从一组语言模型中构建单个静态语言模型,每个模型都在一个单独的文本语料库上训练,同时旨在最大限度地提高作为句子开发集的自适应数据集的可能性。所提出的模型可以看作是混合语言模型的混合。顶层的混合模型是句子级混合模型,其中每个句子都假定是从一组离散的主题或任务集群中提取的。在选择集群之后,假设每个n-gram都是从给定的n-gram语言模型之一中绘制的。我们使用期望最大化(EM)算法估计每个聚类的混合权值和n-gram语言模型的混合权值,以寻求使发展句子的可能性最大化的参数估计。使用先前提出的贝叶斯语言模型插值技术,这种混合模型可以有效地表示为静态n-gram语言模型。与标准的一级插值方案相比,我们展示了该技术的显着改进(包括困惑度和WER)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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