@inproceedings{mosquera-2022-amsqr,
title = "Amsqr at {S}em{E}val-2022 Task 4: Towards {A}uto{NLP} via Meta-Learning and Adversarial Data Augmentation for {PCL} Detection",
author = "Mosquera, Alejandro",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.66/",
doi = "10.18653/v1/2022.semeval-1.66",
pages = "485--489",
abstract = "This paper describes the use of AutoNLP techniques applied to the detection of patronizing and condescending language (PCL) in a binary classification scenario. The proposed approach combines meta-learning, in order to identify the best performing combination of deep learning architectures, with the synthesis of adversarial training examples; thus boosting robustness and model generalization. A submission from this system was evaluated as part of the first sub-task of SemEval 2022 - Task 4 and achieved an F1 score of 0.57{\%}, which is 16 percentage points higher than the RoBERTa baseline provided by the organizers."
}
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<abstract>This paper describes the use of AutoNLP techniques applied to the detection of patronizing and condescending language (PCL) in a binary classification scenario. The proposed approach combines meta-learning, in order to identify the best performing combination of deep learning architectures, with the synthesis of adversarial training examples; thus boosting robustness and model generalization. A submission from this system was evaluated as part of the first sub-task of SemEval 2022 - Task 4 and achieved an F1 score of 0.57%, which is 16 percentage points higher than the RoBERTa baseline provided by the organizers.</abstract>
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%0 Conference Proceedings
%T Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection
%A Mosquera, Alejandro
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F mosquera-2022-amsqr
%X This paper describes the use of AutoNLP techniques applied to the detection of patronizing and condescending language (PCL) in a binary classification scenario. The proposed approach combines meta-learning, in order to identify the best performing combination of deep learning architectures, with the synthesis of adversarial training examples; thus boosting robustness and model generalization. A submission from this system was evaluated as part of the first sub-task of SemEval 2022 - Task 4 and achieved an F1 score of 0.57%, which is 16 percentage points higher than the RoBERTa baseline provided by the organizers.
%R 10.18653/v1/2022.semeval-1.66
%U https://aclanthology.org/2022.semeval-1.66/
%U https://doi.org/10.18653/v1/2022.semeval-1.66
%P 485-489
Markdown (Informal)
[Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection](https://aclanthology.org/2022.semeval-1.66/) (Mosquera, SemEval 2022)
ACL