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Simple Search
Hits 1 – 12 of 12
1
Analysis of Language Change in Collaborative Instruction Following ...
Effenberger, Anna
;
Yan, Eva
;
Singh, Rhia
. - : University of Massachusetts Amherst, 2022
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2
Analysis of Language Change in Collaborative Instruction Following
Effenberger, Anna
;
Yan, Eva
;
Singh, Rhia
...
In: Proceedings of the Society for Computation in Linguistics (2022)
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3
Analysis of Language Change in Collaborative Instruction Following ...
Effenberger, Anna
;
Yan, Eva
;
Singh, Rhia
. - : arXiv, 2021
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4
Analysis of Language Change in Collaborative Instruction Following ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Artzi, Yoav
;
Effenberger, Anna
. - : Underline Science Inc., 2021
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5
Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Artzi, Yoav
;
Kojima, Noriyuki
. - : Underline Science Inc., 2021
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6
Evaluating Models' Local Decision Boundaries via Contrast Sets ...
Gardner, Matt
;
Artzi, Yoav
;
Basmova, Victoria
. - : arXiv, 2020
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7
What is Learned in Visually Grounded Neural Syntax Acquisition ...
Kojima, Noriyuki
;
Averbuch-Elor, Hadar
;
Rush, Alexander M.
. - : arXiv, 2020
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8
A Corpus for Reasoning About Natural Language Grounded in Photographs ...
Suhr, Alane
;
Zhou, Stephanie
;
Zhang, Ally
. - : arXiv, 2018
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9
Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments ...
Chen, Howard
;
Suhr, Alane
;
Misra, Dipendra
. - : arXiv, 2018
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10
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning ...
Misra, Dipendra
;
Langford, John
;
Artzi, Yoav
. - : arXiv, 2017
Abstract:
We propose to directly map raw visual observations and text input to actions for instruction execution. While existing approaches assume access to structured environment representations or use a pipeline of separately trained models, we learn a single model to jointly reason about linguistic and visual input. We use reinforcement learning in a contextual bandit setting to train a neural network agent. To guide the agent's exploration, we use reward shaping with different forms of supervision. Our approach does not require intermediate representations, planning procedures, or training different models. We evaluate in a simulated environment, and show significant improvements over supervised learning and common reinforcement learning variants. ... : In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017 ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://arxiv.org/abs/1704.08795
https://dx.doi.org/10.48550/arxiv.1704.08795
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11
Situated Learning and Understanding of Natural Language
Artzi, Yoav
. - 2015
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12
Learning to Automatically Solve Algebra Word Problems
Kushman, Nate
;
Artzi, Yoav
;
Zettlemoyer, Luke
...
In: MIT web domain (2014)
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