We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
A Neural Local Coherence Model
Dat Nguyen*, and Shafiq Joty*. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL'17) , pages 1320-1330, 2017.
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