Abstract
This paper describes a system built in the SemEval-2022 competition. As participants in Task 4: Patronizing and Condescending Language Detection, we implemented the text sentiment classification system for two subtasks in English. Both subtasks involve determining emotions; subtask 1 requires us to determine whether the text belongs to the PCL category (single-label classification), and subtask 2 requires us to determine to which PCL category the text belongs (multi-label classification). Our system is based on the bidirectional encoder representations from transformers (BERT) model. For the single-label classification, our system applies a BertForSequenceClassification model to classify the input text. For the multi-label classification, we use the fine-tuned BERT model to extract the sentiment score of the text and a fully connected layer to classify the text into the PCL categories. Our system achieved relatively good results on the competition’s official leaderboard.- Anthology ID:
- 2022.semeval-1.61
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 454–458
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.61
- DOI:
- 10.18653/v1/2022.semeval-1.61
- Cite (ACL):
- Wenqiang Bai, Jin Wang, and Xuejie Zhang. 2022. YNU-HPCC at SemEval-2022 Task 4: Finetuning Pretrained Language Models for Patronizing and Condescending Language Detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 454–458, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- YNU-HPCC at SemEval-2022 Task 4: Finetuning Pretrained Language Models for Patronizing and Condescending Language Detection (Bai et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.semeval-1.61.pdf