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.- Anthology ID:
- 2022.semeval-1.66
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 485–489
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.66
- DOI:
- 10.18653/v1/2022.semeval-1.66
- Cite (ACL):
- Alejandro Mosquera. 2022. Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 485–489, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection (Mosquera, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.semeval-1.66.pdf
- Data
- DPM