Neural symbolic machines: Learning semantic parsers on freebase with weak supervision

ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)

Volumn 1, Issue , 2017, Pages 23-33

  Liang, Chen ^a,b,c   Berant, Jonathan ^a,b,c   Le, Quoc ^a,b,c   Forbus, Kenneth D ^a,b,c   Lao, Ni ^a,b,c  

^a NORTHWESTERN UNIVERSITY   (United States)

^b TEL AVIV UNIVERSITY   (Israel)

^c GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; DIGITAL STORAGE; KNOWLEDGE BASED SYSTEMS; LINGUISTICS; MAXIMUM LIKELIHOOD; PROGRAM INTERPRETERS; SEMANTICS; SPEECH RECOGNITION;

DOMAIN-SPECIFIC KNOWLEDGE; FEATURE ENGINEERINGS; LANGUAGE UNDERSTANDING; LEARNING SEMANTICS; QUESTION-ANSWER PAIRS; STATISTICAL POWER; STRUCTURED PREDICTION; SYMBOLIC REASONING;

LISP (PROGRAMMING LANGUAGE);

EID: 85040923863     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/P17-1003     Document Type: Conference Paper

References (44)

1. Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016. Learning to compose neural networks for question answering. CoRR abs/1601.01705.
2. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473. http://arxiv.org/abs/1409.0473.
3. Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. Semantic parsing on freebase from question-answer pairs. In EMNLP. volume 2, page 6.
4. Jonathan Berant and Percy Liang. 2015. Imitation learning of agenda-based semantic parsers. TACL 3:545-558.
5. K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In International Conference on Management of Data (SIGMOD). pages 1247-1250.
6. Kyunghyun Cho, Bart van Merrienboer, Caglar Gul-cehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Doha, Qatar, pages 1724-1734. http://www.aclweb.org/anthology/D141179.
7. H. Daume, J. Langford, and D. Marcu. 2009. Search-based structured prediction. Machine Learning 75:297-325.
8. Li Dong and Mirella Lapata. 2016. Language to logical form with neural attention. In Association for Computational Linguistics (ACL).
9. Alex Graves, Greg Wayne, and Ivo Danihelka. 2014. Neural turing machines. arXiv preprint arXiv:1410.5401.
10. Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwinska, Sergio G. Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià P. Badia, Karl M. Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu, and Demis Hassabis. 2016a. Hybrid computing using a neural network with dynamic external memory. Nature advance online publication. https://doi.org/10.1038/nature20101.
11. Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwinska, ´ Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, et al. 2016b. Hybrid computing using a neural network with dynamic external memory. Nature.
12. Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29(6):82-97.
13. Robin Jia and Percy Liang. 2016. Data recombination for neural semantic parsing. In Association for Computational Linguistics (ACL).
14. J. Jiang, A. Teichert, J. Eisner, and H. Daume. 2012. Learned prioritization for trading off accuracy and speed. In Advances in Neural Information Processing Systems (NIPS).
15. Lukasz Kaiser and Ilya Sutskever. 2015. Neural gpus learn algorithms. arXiv preprint arXiv:1511.08228.
16. Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. CoRR abs/1412.6980. http://arxiv.org/abs/1412.6980.
17. Ni Lao, Tom Mitchell, and William W Cohen. 2011. Random walk inference and learning in a large scale knowledge base. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pages 529-539.
18. Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky. 2016. Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541.
19. P. Liang, M. I. Jordan, and D. Klein. 2011. Learning dependency-based compositional semantics. In Association for Computational Linguistics (ACL). pages 590-599.
20. Arvind Neelakantan, Quoc V. Le, and Ilya Sutskever. 2015. Neural programmer: Inducing latent programs with gradient descent. CoRR abs/1511.04834.
21. Mohammad Norouzi, Samy Bengio, zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, and Dale Schuurmans. 2016. Reward augmented maximum likelihood for neural structured prediction. In Advances in Neural Information Processing Systems (NIPS).
22. Panupong Pasupat and Percy Liang. 2015. Compositional semantic parsing on semi-structured tables. In ACL.
23. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In EMNLP.
24. Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2015. Sequence level training with recurrent neural networks. arXiv preprint arXiv:1511.06732.
25. Scott Reed and Nando de Freitas. 2016. Neural programmer-interpreters. In ICLR.
26. S. Ross, G. Gordon, and A. Bagnell. 2011. A reduction of imitation learning and structured prediction to no-regret online learning. In Artificial Intelligence and Statistics (AISTATS).
27. Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2016. Prioritized experience replay. In International Conference on Learning Representations. Puerto Rico.
28. Jürgen Schmidhuber. 2004. Optimal ordered problem solver. Machine Learning 54(3):211-254.
29. David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Pan-neershelvam, Marc Lanctot, et al. 2016. Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484-489.
30. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems. pages 3104-3112.
31. Richard S. Sutton and Andrew G. Barto. 1998. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA.
32. Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer networks. In Advances in Neural Information Processing Systems (NIPS).
33. Yushi Wang, Jonathan Berant, and Percy Liang. 2015. Building a semantic parser overnight. In Association for Computational Linguistics (ACL).
34. Jason Weston, Sumit Chopra, and Antoine Bor-des. 2014. Memory networks. arXiv preprint arXiv:1410.3916.
35. Ronald J. Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. In Machine Learning. pages 229-256.
36. Sam Wiseman and Alexander M. Rush. 2016. Sequence-to-sequence learning as beam-search optimization. CoRR abs/1606.02960. http://arxiv.org/abs/1606.02960.
37. Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. CoRR abs/1609.08144. http://arxiv.org/abs/1609.08144.
38. Wen-tau Yih, Ming-Wei Chang, Xiaodong He, and Jianfeng Gao. 2015. Semantic parsing via staged query graph generation: Question answering with knowledge base. In Association for Computational Linguistics (ACL).
39. Wen-tau Yih, Matthew Richardson, Chris Meek, Ming-Wei Chang, and Jina Suh. 2016. The value of semantic parse labeling for knowledge base question answering. In Association for Computational Linguistics (ACL).
40. Pengcheng Yin, Zhengdong Lu, Hang Li, and Ben Kao. 2015. Neural enquirer: Learning to query tables. arXiv preprint arXiv:1512.00965.
41. Adam Yu, Hongrae Lee, and Quoc Le. 2017. Learning to skim text. In ACL.
42. Wojciech Zaremba and Ilya Sutskever. 2015. Reinforcement learning neural turing machines. arXiv preprint arXiv:1505.00521.
43. M. Zelle and R. J. Mooney. 1996. Learning to parse database queries using inductive logic programming. In Association for the Advancement of Artificial Intelligence (AAAI). pages 1050-1055.
44. L. S. Zettlemoyer and M. Collins. 2005. Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In Uncertainty in Artificial Intelligence (UAI). pages 658-666.