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Hits 1 – 20 of 33
1
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation ...
Liu, Alisa
;
Swayamdipta, Swabha
;
Smith, Noah A.
. - : arXiv, 2022
BASE
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2
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics ...
Qin, Lianhui
;
Welleck, Sean
;
Khashabi, Daniel
. - : arXiv, 2022
BASE
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3
Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection ...
Sap, Maarten
;
Swayamdipta, Swabha
;
Vianna, Laura
. - : arXiv, 2021
BASE
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4
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World ...
Zellers, Rowan
;
Holtzman, Ari
;
Peters, Matthew
. - : arXiv, 2021
BASE
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5
Contrastive Explanations for Model Interpretability ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Choi, Yejin
;
Elazar, Yanai
. - : Underline Science Inc., 2021
BASE
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6
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
., Matthew
;
Choi, Yejin
. - : Underline Science Inc., 2021
BASE
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7
Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bhagavatula, Chandra
;
Choi, Yejin
. - : Underline Science Inc., 2021
BASE
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8
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Arabshahi, Forough
;
Bosselut, Antoine
. - : Underline Science Inc., 2021
BASE
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9
Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bosselut, antoineb@allenai.org
;
Choi, Yejin
. - : Underline Science Inc., 2021
BASE
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10
GO FIGURE: A Meta Evaluation of Factuality in Summarization ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Celikyilmaz, Asli
;
Choi, Yejin
. - : Underline Science Inc., 2021
BASE
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11
CLIPScore: A Reference-free Evaluation Metric for Image Captioning ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Choi, Yejin
;
Forbes, Maxwell
. - : Underline Science Inc., 2021
BASE
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12
Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Choi, Yejin
;
D. Hwang, Jena
. - : Underline Science Inc., 2021
BASE
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13
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bhagavatula, Chandra
;
Choi, Yejin
. - : Underline Science Inc., 2021
BASE
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14
Challenges in Automated Debiasing for Toxic Language Detection ...
Zhou, Xuhui
;
Sap, Maarten
;
Swayamdipta, Swabha
. - : arXiv, 2021
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15
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics ...
Lu, Ximing
;
Welleck, Sean
;
West, Peter
. - : arXiv, 2021
BASE
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16
From Physical to Social Commonsense: Natural Language and the Natural World
Forbes, Maxwell
. - 2021
BASE
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17
Positive AI with Social Commonsense Models
Sap, Maarten
. - 2021
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18
Generative Data Augmentation for Commonsense Reasoning ...
Yang, Yiben
;
Malaviya, Chaitanya
;
Fernandez, Jared
;
Swayamdipta, Swabha
;
Bras, Ronan Le
;
Wang, Ji-Ping
;
Bhagavatula, Chandra
;
Choi, Yejin
;
Downey, Doug
. - : arXiv, 2020
Abstract:
Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance. However, manual curation of training examples is expensive and has been shown to introduce annotation artifacts that neural models can readily exploit and overfit on. We investigate G-DAUG^C, a novel generative data augmentation method that aims to achieve more accurate and robust learning in the low-resource setting. Our approach generates synthetic examples using pretrained language models, and selects the most informative and diverse set of examples for data augmentation. In experiments with multiple commonsense reasoning benchmarks, G-DAUG^C consistently outperforms existing data augmentation methods based on back-translation, and establishes a new state-of-the-art on WinoGrande, CODAH, and CommonsenseQA. Further, in addition to improvements in in-distribution accuracy, G-DAUG^C-augmented training also enhances out-of-distribution generalization, showing greater robustness against ... : Findings of the Association for Computational Linguistics: EMNLP 2020 ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://arxiv.org/abs/2004.11546
https://dx.doi.org/10.48550/arxiv.2004.11546
BASE
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19
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints ...
Lu, Ximing
;
West, Peter
;
Zellers, Rowan
. - : arXiv, 2020
BASE
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20
Understanding Natural Language with Commonsense Knowledge Representation, Reasoning, and Simulation
Bosselut, Antoine
. - 2020
BASE
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