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As neural networks have dominated the state-of-the-art results in a wide range of NLP tasks, it attracts considerable attention to improve the performance of neural models by integrating symbolic knowledge.
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Neural-symbolic learning and reasoning: contributions and challenges
Garcez, A., Besold, T. R., De Raedt, L., Földiak, P., Hitzler, P., Icard, T., Kühnberger, K.-U., Lamb, L. C., Miikkulainen, R., and Silver, D. L. (2015) · 2015
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Harnessing deep neural networks with logic rules
Hu, Z., Ma, X., Liu, Z., Hovy, E., and Xing, E. (2016) · 2016
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Neural-symbolic learning and reasoning: A survey and interpretation
Besold, T. R., Garcez, A. d., Bader, S., Bowman, H., Domingos, P., Hitzler, P., Kühnberger, K.-U., Lamb, L. C., Lowd, D., Lima, P. M. V., et al. (2017) · 2017
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Neural module networks
Andreas, J., Rohrbach, M., Darrell, T., and Klein, D. (2016a)
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Learning to compose neural networks for question answering
Andreas, J., Rohrbach, M., Darrell, T., and Klein, D. (2016b)
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Lu, Z., Liu, X., Cui, H., Yan, Y., and Zheng, D. (2018) · 2018
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Marrying up regular expressions with neural networks: A case study for spoken language understanding
Luo, B., Feng, Y., Wang, Z., Huang, S., Yan, R., and Zhao, D. (2018) · 2093
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