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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 (6)
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Hits 1 – 20 of 34
1
Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction ...
Huang, Kuan-Hao
;
Hsu, I-Hung
;
Natarajan, Premkumar
. - : arXiv, 2022
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2
Cross-lingual Representation Learning for Natural Language Processing
Ahmad, Wasi Uddin
. - : eScholarship, University of California, 2021
BASE
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3
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? ...
Zhao, Jieyu
;
Khashabi, Daniel
;
Khot, Tushar
. - : arXiv, 2021
BASE
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4
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training ...
Huang, Kuan-Hao
;
Ahmad, Wasi Uddin
;
Peng, Nanyun
. - : arXiv, 2021
BASE
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5
Societal Biases in Language Generation: Progress and Challenges ...
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
;
Chang, Kai-Wei
;
Natarajan, Prem
. - : Underline Science Inc., 2021
BASE
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6
Socially Aware Bias Measurements for Hindi Language Representations ...
Malik, Vijit
;
Dev, Sunipa
;
Nishi, Akihiro
. - : arXiv, 2021
BASE
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7
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Chang, Kai-Wei
;
Hsieh, Cho-Jui
;
Li, Zongyi
;
Li, Linyang
;
Xu, Jianhan
;
Zeng, Jiehang
;
Zhang, Qi
;
Zheng, Xiaoqing
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.251/ Abstract: Recent studies have shown that deep neural network-based models are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models. However, there is a lack of systematic study on comparing different defense approaches under the same attacking setting. In this paper, we seek to fill the gap through comprehensive studies on the behavior of neural text classifiers trained with various defense methods against representative adversarial attacks. In addition, we propose an effective method to further improve the robustness of neural text classifiers against such attacks, and achieved the highest accuracy on both clean and adversarial examples on AGNEWS and IMDB datasets, outperforming existing methods by a significant margin. We hope this study could provide useful clues for future research on text adversarial defense. Codes ...
Keyword:
Computational Linguistics
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural Language Processing
;
Neural Network
URL:
https://dx.doi.org/10.48448/tmch-e809
https://underline.io/lecture/38025-searching-for-an-effective-defender-benchmarking-defense-against-adversarial-word-substitution
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8
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification ...
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
;
Chang, Kai-Wei
;
Dhamala, Jwala
. - : Underline Science Inc., 2021
BASE
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9
Intent Classification and Slot Filling for Privacy Policies ...
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
;
Ahmad, Wasi Uddin
;
Chang, Kai-Wei
. - : Underline Science Inc., 2021
BASE
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10
Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Chang, Kai-Wei
;
Hu, Ziniu
. - : Underline Science Inc., 2021
BASE
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11
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? ...
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
;
Chang, Kai-Wei
;
Khashabi, Daniel
. - : Underline Science Inc., 2021
BASE
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12
Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble ...
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
;
Chang, Kai-Wei
;
Hsieh, Cho-Jui
. - : Underline Science Inc., 2021
BASE
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13
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Ahmad, Wasi Uddin
;
Chang, Kai-Wei
. - : Underline Science Inc., 2021
BASE
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14
Syntax-augmented Multilingual BERT for Cross-lingual Transfer ...
Ahmad, Wasi Uddin
;
Li, Haoran
;
Chang, Kai-Wei
. - : arXiv, 2021
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15
Syntax-augmented Multilingual BERT for Cross-lingual Transfer ...
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
;
Ahmad, Wasi Uddin
;
Chang, Kai-Wei
. - : Underline Science Inc., 2021
BASE
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16
BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation ...
Dhamala, Jwala
;
Sun, Tony
;
Kumar, Varun
. - : arXiv, 2021
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17
"The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition ...
Zhou, Yichao
;
Jiang, Jyun-Yu
;
Zhao, Jieyu
. - : arXiv, 2020
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18
Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer ...
Zhao, Jieyu
;
Mukherjee, Subhabrata
;
Hosseini, Saghar
. - : arXiv, 2020
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19
On the Robustness of Language Encoders against Grammatical Errors ...
Yin, Fan
;
Long, Quanyu
;
Meng, Tao
. - : arXiv, 2020
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20
GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction ...
Ahmad, Wasi Uddin
;
Peng, Nanyun
;
Chang, Kai-Wei
. - : arXiv, 2020
BASE
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