Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

ACL 2017 Chen LiangJonathan BerantQuoc LeKenneth D. ForbusNi Lao

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural "programmer", i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic "computer", i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space... (read more)

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