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Harnessing the statistical power of neural networks to perform languageunderstanding and symbolic reasoning is difficult, when it requires executingefficient discrete operations against a large knowledge-base. In this work, weintroduce a Neural Symbolic Machine, which contains (a) a neural "programmer",i.e., a sequence-to-sequence model that maps language utterances to programsand utilizes a key-variable memory to handle compositionality (b) a symbolic"computer", i.e., a Lisp interpreter that performs program execution, and helpsfind good programs by pruning the search space. We apply REINFORCE to directlyoptimize the task reward of this structured prediction problem. To train withweak supervision and improve the stability of REINFORCE, we augment it with aniterative maximum-likelihood training process. NSM outperforms thestate-of-the-art on the WebQuestionsSP dataset when trained fromquestion-answer pairs only, without requiring any feature engineering ordomain-specific knowledge.

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