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.
CITATION STYLE
Lin, R., Yang, M., Liu, S., Li, S., & Zhao, T. (2015). A maximum entropy approach to discourse coherence modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9362, pp. 3–11). Springer Verlag. https://doi.org/10.1007/978-3-319-25207-0_1
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