A Maximum Entropy Approach to Discourse Coherence Modeling
Rui Lin, Muyun Yang, Shujie Liu, Sheng Li, Tiejun Zhao · Lecture notes in computer science · 2015
This paper introduces a maximum entropy method to Discourse Coherence Modeling (DCM). Different from the state-of-art supervised entity-grid model and unsupervised cohesion-driven model, the model we proposed only takes as input lexicon features, which increases the training speed and decoding speed significantly. We conduct an evaluation on two publicly available benchmark data sets via sentence ordering tasks, and the results confirm the effectiveness of our maximum entropy based approach in DCM.