# Explainable natural language processing with matrix product states

@article{Tangpanitanon2021ExplainableNL, title={Explainable natural language processing with matrix product states}, author={J. Tangpanitanon and Chanatip Mangkang and Pradeep Bhadola and Yuichiro Minato and Dimitris G Angelakis and Thiparat Chotibut}, journal={New Journal of Physics}, year={2021}, volume={24} }

Despite empirical successes of recurrent neural networks (RNNs) in natural language processing (NLP), theoretical understanding of RNNs is still limited due to intrinsically complex non-linear computations. We systematically analyze RNNs’ behaviors in a ubiquitous NLP task, the sentiment analysis of movie reviews, via the mapping between a class of RNNs called recurrent arithmetic circuits (RACs) and a matrix product state. Using the von-Neumann entanglement entropy (EE) as a proxy for…

## One Citation

### Group-invariant tensor train networks for supervised learning

- Computer ScienceArXiv
- 2022

A new numerical algorithm is introduced to construct a basis of tensors that are invariant under the action of normal matrix representations of an arbitrary discrete group, which can be up to several orders of magnitude faster than previous approaches.

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