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The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (1)
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Simple Search
Hits 1 – 8 of 8
1
Trajectory Prediction with Linguistic Representations
Kuo, Yen-Ling
;
Huang, Xin
;
Barbu, Andrei
. - : Center for Brains, Minds and Machines (CBMM), International Conference on Robotics and Automation (ICRA), 2022
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2
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding
Kuo, Yen-Ling
;
Katz, Boris
;
Barbu, Andrei
. - : Center for Brains, Minds and Machines (CBMM), Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
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3
Trajectory Prediction with Linguistic Representations ...
Kuo, Yen-Ling
;
Huang, Xin
;
Barbu, Andrei
. - : arXiv, 2021
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4
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Barbu, Andrei
;
Katz, Boris
. - : Underline Science Inc., 2021
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5
Learning a natural-language to LTL executable semantic parser for grounded robotics
Wang, Christopher
;
Ross, Candace
;
Kuo, Yen-Ling
. - : Center for Brains, Minds and Machines (CBMM), Conference on Robot Learning (CoRL), 2020
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6
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ...
Kuo, Yen-Ling
;
Katz, Boris
;
Barbu, Andrei
. - : arXiv, 2020
Abstract:
Humans are remarkably flexible when understanding new sentences that include combinations of concepts they have never encountered before. Recent work has shown that while deep networks can mimic some human language abilities when presented with novel sentences, systematic variation uncovers the limitations in the language-understanding abilities of networks. We demonstrate that these limitations can be overcome by addressing the generalization challenges in the gSCAN dataset, which explicitly measures how well an agent is able to interpret novel linguistic commands grounded in vision, e.g., novel pairings of adjectives and nouns. The key principle we employ is compositionality: that the compositional structure of networks should reflect the compositional structure of the problem domain they address, while allowing other parameters to be learned end-to-end. We build a general-purpose mechanism that enables agents to generalize their language understanding to compositional domains. Crucially, our network has ... : Accepted in Findings of EMNLP 2021 ...
Keyword:
Artificial Intelligence cs.AI
;
Computation and Language cs.CL
;
FOS Computer and information sciences
;
Robotics cs.RO
URL:
https://arxiv.org/abs/2008.02742
https://dx.doi.org/10.48550/arxiv.2008.02742
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7
Learning a natural-language to LTL executable semantic parser for grounded robotics ...
Wang, Christopher
;
Ross, Candace
;
Kuo, Yen-Ling
. - : arXiv, 2020
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8
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
Kuo, Yen-Ling
;
Katz, Boris
;
Barbu, Andrei
. - : Center for Brains, Minds and Machines (CBMM), The Ninth International Conference on Learning Representations (ICLR), 2020
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