A paper is the tip of an iceberg

I was reading Clark and Manning (2016) and studying their code. The contrast is just amazing.

This is what the paper has to say:

architecture

This is what I found after 1 hour of reading a JSON file and writing down all layers of the neural net (the file is data/models/all_pairs/architecture.json, created when you run the experiment):

deep-coref.png
Without the source code, this would be a replication nightmare for sure.

References

Clark, K., & Manning, C. D. (2016). Improving Coreference Resolution by Learning Entity-Level Distributed Representations. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 643–653. http://doi.org/10.18653/v1/P16-1061

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