The emergent algebraic structure of RNNs and embeddings in NLP [article]

Sean A. Cantrell
2018 arXiv   pre-print
We examine the algebraic and geometric properties of a uni-directional GRU and word embeddings trained end-to-end on a text classification task. A hyperparameter search over word embedding dimension, GRU hidden dimension, and a linear combination of the GRU outputs is performed. We conclude that words naturally embed themselves in a Lie group and that RNNs form a nonlinear representation of the group. Appealing to these results, we propose a novel class of recurrent-like neural networks and a word embedding scheme.
arXiv:1803.02839v1 fatcat:dh4o3hc7i5erngnd56wt6brtxa