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.
Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering
Shafiq Joty, Preslav Nakov, Lluís Màrquez, and Israa Jaradat. In Proceedings of The SIGNLL Conference on Computational Natural Language Learning (CoNLL'17) , pages 226-237, 2017.
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