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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
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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
;
Barzilay, Regina
In: MIT web domain (2014)
Abstract:
We present an approach for automatically learning to solve algebra word problems. Our algorithm reasons across sentence boundaries to construct and solve a system of linear equations, while simultaneously recovering an alignment of the variables and numbers in these equations to the problem text. The learning algorithm uses varied supervision, including either full equations or just the final answers. We evaluate performance on a newly gathered corpus of algebra word problems, demonstrating that the system can correctly answer almost 70% of the questions in the dataset. This is, to our knowledge, the first learning result for this task. ; Battelle Memorial Institute (PO 300662) ; National Science Foundation (U.S.) (Grant IIS-0835652)
URL:
http://hdl.handle.net/1721.1/99744
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