Exponential Language Models, Logistic Regression, and Semantic Coherence
2018-06-30T07:05:56Z (GMT) by
In this paper, we modify the traditional trigram model by using utterance-level semantic coherence features in an exponential model. The semantic coherence features are collected by measuring the correlations among content-word pairs occurring in sentences of two corpora, the real corpus and a corpus generated by the baseline trigram model. The measure we use for estimating the semantic association of content word pairs is Yule's Q statistic. For our preliminary analysis, we have further simplified the modeling task by extracting a small set of statistics from each sentence-based Q statistics and applying them as features to the exponential model. We also simplified the process of obtaining the MLE solutions of the exponential models by approximating it with a logistic regression model. We account for the uncertainty in the estimates of Q by constructing confidence intervals. The new model results in a slight reduction in test-set perplexity.