Kenneth Forbus

Also published as: Kenneth D. Forbus


pdf bib
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
Chen Liang | Jonathan Berant | Quoc Le | Kenneth D. Forbus | Ni Lao
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.


pdf bib
NULEX: An Open-License Broad Coverage Lexicon
Clifton McFate | Kenneth Forbus
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


pdf bib
Analogical Dialogue Acts: Supporting Learning by Reading Analogies
David Barbella | Kenneth Forbus
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading