Fetching the paper…
Reading the bibliography…
Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Learning to map sentences to logical form: structured classification with probabilistic categorial grammars
Luke S Zettlemoyer and Michael Collins. 2005 · 2005
Earlier work this paper cites.
Learning synchronous grammars for semantic parsing with lambda calculus
Yuk Wah Wong and Raymond Mooney. 2007 · 2007
Earlier work this paper cites.
Online learning of relaxed ccg grammars for parsing to logical form
Luke Zettlemoyer and Michael Collins. 2007 · 2007
Earlier work this paper cites.
A generative model for parsing natural language to meaning representations
Wei Lu, Hwee Tou Ng, Wee Sun Lee, and Luke S Zettlemoyer. 2008 · 2008
Earlier work this paper cites.
Graphical models, exponential families, and variational inference
Martin J Wainwright, Michael I Jordan, et al. 2008 · 2008
Earlier work this paper cites.
Inducing probabilistic ccg grammars from logical form with higher-order unification
Tom Kwiatkowski, Luke Zettlemoyer, Sharon Goldwater, and Mark Steedman. 2010 · 2010
Earlier work this paper cites.
Learning dependency-based compositional semantics
P. Liang, M. I. Jordan, and D. Klein. 2011 · 2011
Earlier work this paper cites.
Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
Earlier work this paper cites.
Alex Graves, Greg Wayne, and Ivo Danihelka. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Learning to transduce with unbounded memory
Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015 · 2015
Cited alongside, same era.
Compositional semantic parsing on semi-structured tables
P. Pasupat and P. Liang. 2015 · 2015
Cited alongside, same era.
Efficient inference and structured learning for semantic role labeling
Oscar Täckström, Kuzman Ganchev, and Dipanjan Das. 2015 · 2015
Cited alongside, same era.
Language to logical form with neural attention
Li Dong and Mirella Lapata. 2016 · 2016
Cited alongside, same era.
Data recombination for neural semantic parsing
Robin Jia and Percy Liang. 2016 · 2016
Cited alongside, same era.
Inferring logical forms from denotations
Panupong Pasupat and Percy Liang. 2016 · 2016
Cited alongside, same era.
Macro grammars and holistic triggering for efficient semantic parsing
Yuchen Zhang, Panupong Pasupat, and Percy Liang. 2017 · 2017
Later among the works it cites.
Improving text-to-sql evaluation methodology
Li Zhang Karthik Ramanathan Sesh Sadasivam Rui Zhang Catherine Finegan-Dollak, Jonathan K. Kummerfeld and Dragomir Radev. 2018 · 2018
Later among the works it cites.
Coarse-to-fine decoding for neural semantic parsing
Li Dong and Mirella Lapata. 2018 · 2018
Later among the works it cites.
Weakly supervised semantic parsing with abstract examples
Omer Goldman, Veronica Latcinnik, Ehud Nave, Amir Globerson, and Jonathan Berant. 2018 · 2018
Later among the works it cites.
Neural multi-step reasoning for question answering on semi-structured tables
Till Haug, Octavian-Eugen Ganea, and Paulina Grnarova. 2018 · 2018
Later among the works it cites.
Decoupling structure and lexicon for zero-shot semantic parsing
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Online segment to segment neural transduction
Lei Yu, Jan Buys, and Phil Blunsom. 2016 · 2016
Cited alongside, same era.
Allennlp: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer. 2017 · 2017
Cited alongside, same era.
From language to programs: Bridging reinforcement learning and maximum marginal likelihood
Kelvin Guu, Panupong Pasupat, Evan Zheran Liu, and Percy Liang. 2017 · 2017
Cited alongside, same era.
Structured attention networks
Yoon Kim, Carl Denton, Luong Hoang, and Alexander M Rush. 2017 · 2017
Cited alongside, same era.
Neural semantic parsing with type constraints for semi-structured tables
Jayant Krishnamurthy, Pradeep Dasigi, and Matt Gardner. 2017 · 2017
Cited alongside, same era.
Learning a natural language interface with neural programmer
Arvind Neelakantan, Quoc V Le, Martin Abadi, Andrew McCallum, and Dario Amodei. 2017 · 2017
Cited alongside, same era.
Jonathan Herzig and Jonathan Berant. 2018 · 2018
Later among the works it cites.
Natural language to structured query generation via meta-learning
Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen tau Yih, and Xiaodong He. 2018 · 2018
Later among the works it cites.
Memory augmented policy optimization for program synthesis and semantic parsing
Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V Le, and Ni Lao. 2018 · 2018
Later among the works it cites.
Amr parsing as graph prediction with latent alignment
Chunchuan Lyu and Ivan Titov. 2018 · 2018
Later among the works it cites.
Incsql: Training incremental text-to-sql parsers with non-deterministic oracles
Tianze Shi, Kedar Tatwawadi, Kaushik Chakrabarti, Yi Mao, Oleksandr Polozov, and Weizhu Chen. 2018 · 2018
Later among the works it cites.
Semantic parsing with syntax-and table-aware sql generation
Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, and Ming Zhou. 2018 · 2018
Later among the works it cites.
Typesql: Knowledge-based type-aware neural text-to-sql generation
Tao Yu, Zifan Li, Zilin Zhang, Rui Zhang, and Dragomir Radev. 2018 · 2018
Later among the works it cites.
Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2018
Later among the works it cites.
Learning to generalize from sparse and underspecified rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans, and Mohammad Norouzi. 2019 · 2019
Closest in time.
Iterative search for weakly supervised semantic parsing
Pradeep Dasigi, Matt Gardner, Shikhar Murty, Luke Zettlemoyer, and Eduard Hovy. 2019 · 2019
Closest in time.
Learning to infer program sketches
Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, and Armando Solar-Lezama. 2019 · 2019
Closest in time.