Grounding natural language instructions to semantic goal representations for abstraction and generalization

Author:

Arumugam Dilip,Karamcheti SiddharthORCID,Gopalan Nakul,Williams Edward C.,Rhee Mina,Wong Lawson L. S.,Tellex Stefanie

Funder

National Science Foundation

National Aeronautics and Space Administration

Defense Advanced Research Projects Agency

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference42 articles.

1. Artzi, Y, & Zettlemoyer, L. (2013). Weakly supervised learning of semantic parsers for mapping instructions to actions. In Annual meeting of the association for computational linguistics.

2. Arumugam, D., Karamcheti, S., Gopalan, N., Wong, L., & Tellex, S. (2017). Accurately and efficiently interpreting human–robot instructions of varying granularities. In Robotics: Science and systems XIII. https://doi.org/10.15607/rss.2017.xiii.056 .

3. Bellman, R. (1957). Dynamic programming. Princeton: Princeton University Press.

4. Bengio, Y., Ducharme, R., Vincent, P., & Janvin, C. (2000). A neural probabilistic language model. Journal of Machine Learning Research, 3, 1137–1155.

5. Brown, P. F., Cocke, J., Pietra, S. D., Pietra, V. J. D., Jelinek, F., Lafferty, J. D., et al. (1990). A statistical approach to machine translation. Computational Linguistics, 16, 79–85.

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