Dependency language models for sentence completion

Abstract

Sentence completion is a challenging semantic modeling task in which models must choose the most appropriate word from a given set to complete a sentence. Although a variety of language models have been applied to this task in previous work, none of the existing approaches incorporate syntactic information. In this paper we propose to tackle this task using a pair of simple language models in which the probability of a sentence is estimated as the probability of the lexicalisation of a given syntactic dependency tree. We apply our approach to the Microsoft Research Sentence Completion Challenge and show that it improves on n-gram language models by 8.7 percentage points, achieving the highest accuracy reported to date apart from neural language models that are more complex and expensive to train.