Can Neural Networks Learn Symbolic Rewriting?

descriptionPublicationkeyboard_double_arrow_right Conference object , Article , Preprint 01 Jan 2019Embargo end date: 01 Jan 2019Publisher:ZenodoJournal:CoRR, volume abs/1911.04873Funded by:EC | AI4REASON, EC | SMART

Authors: Piotrowski, Bartosz; Urban, Josef; Brown, Chad; Kaliszyk, Cezary;

doi: 10.5281/zenodo.3407218 , 10.48550/arxiv.1911.04873 , 10.5281/zenodo.3407217

arXiv: 1911.04873

Can Neural Networks Learn Symbolic Rewriting?

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Abstract

This work investigates if the current neural architectures are adequate for learning symbolic rewriting. Two kinds of data sets are proposed for this research – one based on automated proofs and the other being a synthetic set of polynomial terms. The experiments with use of the current neural machine translation models are performed and its results are discussed. Ideas for extending this line of research are proposed and its relevance is motivated.

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Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computation and Language (cs.CL), Machine Learning (cs.LG)

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