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Chinese discourse coherence modeling remains a challenge taskin NaturalLanguage Processing field.Existing approaches mostlyfocus on the need forfeature engineering, whichadoptthe sophisticated features to capture the logicor syntactic or semantic relationships acrosssentences within a text.In thispaper, we present an entity-drivenrecursive deep modelfor the Chinese discoursecoherence evaluation based on current English discourse coherenceneural networkmodel. Specifically, to overcome the shortage of identifying the entity(nouns)overlap across sentences in the currentmodel, Our combined modelsuccessfullyinvestigatesthe entities information into the recursive neural networkfreamework.Evaluation results on both sentence ordering and machine translationcoherence rating task show the effectiveness of the proposed model, whichsignificantly outperforms the existing strong baseline.

An entity-driven recursive neural network model for chinese discourse coherence modeling