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

@inproceedings{Liang2016NeuralSM,
  title={Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision},
  author={Chen Liang and Jonathan Berant and Quoc V. Le and Kenneth D. Forbus and Ni Lao},
  booktitle={ACL},
  year={2016}
}
Extending the success of deep neural networks to natural language understanding and symbolic reasoning requires complex operations and external memory. Recent neural program induction approaches have attempted to address this problem, but are typically limited to differentiable memory, and consequently cannot scale beyond small synthetic tasks. In this work, we propose the Manager-ProgrammerComputer framework, which integrates neural networks with non-differentiable memory to support abstract… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 91 CITATIONS, ESTIMATED 39% COVERAGE

FILTER CITATIONS BY YEAR

2017
2019

CITATION STATISTICS

  • 13 Highly Influenced Citations

  • Averaged 41 Citations per year over the last 3 years

  • 33% Increase in citations per year in 2018 over 2017

References

Publications referenced by this paper.
SHOWING 1-10 OF 34 REFERENCES

, Ioannis Antonoglou , Veda Panneershelvam , Marc Lanctot , et al . Mastering the game of go with deep neural networks and tree search

  • Ilya Sutskever, Oriol Vinyals, Quoc V Le
  • Nature
  • 2016

Co-learning of language understanding and reasoning from question-answer

  • M. Crouse, C. McFate, K. Forbus
  • 2016
1 Excerpt

Similar Papers

Loading similar papers…