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Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate the training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU...

...The algorithm was implemented in Java and can be described as a form of a parsing expression grammar, where we generate the expressions to be detected beforehand by regular expressions and store them in a vocabulary. The central class is the parser class, which is instantiated as a series of vocabularies, simple text lists that describe tokens and synonyms, and value descriptors. It's meant as a lightweight alternative to "real" NLP (natural language processing).

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