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Found 4 papers, 2 papers with code
Mapping Natural-language Problems to Formal-language Solutions Using Structured Neural Representations
The encoder of TP-N2F employs TPR `binding' to encode natural-language symbolic structure in vector space and the decoder uses TPR `unbinding' to generate, in symbolic space, a sequential program represented by relational tuples, each consisting of a relation (or operation) and a number of arguments.
Natural- to formal-language generation using Tensor Product Representations
Generating formal-language represented by relational tuples, such as Lisp programs or mathematical expressions, from a natural-language input is an extremely challenging task because it requires to explicitly capture discrete symbolic structural information from the input to generate the output.
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)
In this work, we propose the Manager-Programmer-Computer framework, which integrates neural networks with non-differentiable memory to support abstract, scalable and precise operations through a friendly neural computer interface.
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
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.
