- A Neural Local Coherence Model
- Annual Meeting of the Association for Computational Linguistics
- Book/source title
- The 55th Annual Meeting of the Association for Computational Linguistics
- Book/source subtitle
- proceedings of the Conference : July 30-August 4, 2017, Vancouver, Canada
- Pages (from-to)
- Stroudsburg, PA: Association for Computational Linguistics
- Volume (Publisher)
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
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.
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