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We formalize human language understanding as a structured prediction task where the output is a partially ordered set (poset).
Compositional generalization in a deep seq2seq model by separating syntax and semantics
Russin, J., Jo, J., O’Reilly, R. C., and Bengio, Y. (2019) · 1904
Earlier work this paper cites.
Mul, M. and Zuidema, W. (2019) · 1906
Earlier work this paper cites.
Aspects of the theory of syntax
Chomsky, N. (1965) · 1965
Earlier work this paper cites.
Society of mind
Minsky, M. (1988) · 1988
Earlier work this paper cites.
A systematic comparison of various statistical alignment models
Och, F. J. and Ney, H. (2003) · 2003
Earlier work this paper cites.
Statistical machine translation
Koehn, P. (2009) · 2009
Earlier work this paper cites.
Learning phrase representations using RNN encoder–decoder for statistical machine translation
Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V. (2014) · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y. (2014) · 2015
Earlier work this paper cites.
Order matters: Sequence to sequence for sets
Vinyals, O., Bengio, S., and Kudlur, M. (2015) · 2015
Earlier work this paper cites.
Enhanced lstm for natural language inference
Chen, Q., Zhu, X., Ling, Z., Wei, S., Jiang, H., and Inkpen, D. (2016) · 2016
Earlier work this paper cites.
Language to logical form with neural attention
Dong, L. and Lapata, M. (2016) · 2016
Earlier work this paper cites.
Recurrent neural network grammars
Dyer, C., Kuncoro, A., Ballesteros, M., and Smith, N. A. (2016) · 2016
Earlier work this paper cites.
Robust incremental neural semantic graph parsing
Buys, J. and Blunsom, P. (2017) · 2017
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An incremental parser for abstract meaning representation
Damonte, M., Cohen, S. B., and Satta, G. (2017) · 2017
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A point set generation network for 3d object reconstruction from a single image
Fan, H., Su, H., and Guibas, L. J. (2017) · 2017
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Sqlnet: Generating structured queries from natural language without reinforcement learning
Xu, X., Liu, C., and Song, D. (2017) · 2017
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Zhong, V., Xiong, C., and Socher, R. (2017) · 2017
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Ordered neurons: Integrating tree structures into recurrent neural networks
Shen, Y., Tan, S., Sordoni, A., and Courville, A. (2018) · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Simonovsky, M. and Komodakis, N. (2018) · 2018
Later among the works it cites.
Beyond error propagation in neural machine translation: Characteristics of language also matter
Wu, L., Tan, X., He, D., Tian, F., Qin, T., Lai, J., and Liu, T.-Y. (2018) · 2018
Later among the works it cites.
Holographic and other point set distances for machine learning
Balles, L. and Fischbacher, T. (2019) · 2019
Later among the works it cites.
Semantic graph parsing with recurrent neural network dag grammars
Fancellu, F., Gilroy, S., Lopez, A., and Lapata, M. (2019) · 2019
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Matchzoo: A learning, practicing, and developing system for neural text matching
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Learning representations and generative models for 3d point clouds
Achlioptas, P., Diamanti, O., Mitliagkas, I., and Guibas, L. (2017) · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Lake, B. and Baroni, M. (2018) · 2018
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Rearranging the familiar: Testing compositional generalization in recurrent networks
Loula, J., Baroni, M., and Lake, B. (2018) · 2018
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Amr parsing as graph prediction with latent alignment
Lyu, C. and Titov, I. (2018) · 2018
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Middle-out decoding
Mehri, S. and Sigal, L. (2018) · 2018
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Neural program search: Solving programming tasks from description and examples
Polosukhin, I. and Skidanov, A. (2018) · 2018
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Rezatofighi, S. H., Kaskman, R., Motlagh, F. T., Shi, Q., Cremers, D., Leal-Taixé, L., and Reid, I. (2018) · 2018
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Guo, J., Fan, Y., Ji, X., and Cheng, X. (2019) · 2019
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Unsupervised recurrent neural network grammars
Kim, Y., Rush, A. M., Yu, L., Kuncoro, A., Dyer, C., and Melis, G. (2019) · 2019
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Compositional generalization through meta sequence-to-sequence learning
Lake, B. M. (2019) · 2019
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Compositional generalization for primitive substitutions
Li, Y., Zhao, L., Wang, J., and Hestness, J. (2019) · 2019
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Deep set prediction networks
Zhang, Y., Hare, J., and Prugel-Bennett, A. (2019) · 2019
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Permutation equivariant models for compositional generalization in language
Gordon, J., Lopez-Paz, D., Baroni, M., and Bouchacourt, D. (2020) · 2020
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Measuring compositional generalization: A comprehensive method on realistic data
Keysers, D., Schärli, N., Scales, N., Buisman, H., Furrer, D., Kashubin, S., Momchev, N., Sinopalnikov, D., Stafiniak, L., Tihon, T., Tsarkov, D., Wang, X., van Zee, M., and Bousquet, O. (2020) · 2020
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