Shiyi Li
2024
LMEME at SemEval-2024 Task 4: Teacher Student Fusion - Integrating CLIP with LLMs for Enhanced Persuasion Detection
Shiyi Li
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Yike Wang
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Liang Yang
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Shaowu Zhang
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Hongfei Lin
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
This paper describes our system used in the SemEval-2024 Task 4 Multilingual Detection of Persuasion Techniques in Memes. Our team proposes a detection system that employs a Teacher Student Fusion framework. Initially, a Large Language Model serves as the teacher, engaging in abductive reasoning on multimodal inputs to generate background knowledge on persuasion techniques, assisting in the training of a smaller downstream model. The student model adopts CLIP as an encoder for text and image features, and we incorporate an attention mechanism for modality alignment. Ultimately, our proposed system achieves a Macro-F1 score of 0.8103, ranking 1st out of 20 on the leaderboard of Subtask 2b in English. In Bulgarian, Macedonian and Arabic, our detection capabilities are ranked 1/15, 3/15 and 14/15.
2023
DUTIR at SemEval-2023 Task 10: Semi-supervised Learning for Sexism Detection in English
Bingjie Yu
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Zewen Bai
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Haoran Ji
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Shiyi Li
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Hao Zhang
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Hongfei Lin
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Sexism is an injustice afflicting women and has become a common form of oppression in social media. In recent years, the automatic detection of sexist instances has been utilized to combat this oppression. The Subtask A of SemEval-2023 Task 10, Explainable Detection of Online Sexism, aims to detect whether an English-language post is sexist. In this paper, we describe our system for the competition. The structure of the classification model is based on RoBERTa, and we further pre-train it on the domain corpus. For fine-tuning, we adopt Unsupervised Data Augmentation (UDA), a semi-supervised learning approach, to improve the robustness of the system. Specifically, we employ Easy Data Augmentation (EDA) method as the noising operation for consistency training. We train multiple models based on different hyperparameter settings and adopt the majority voting method to predict the labels of test entries. Our proposed system achieves a Macro-F1 score of 0.8352 and a ranking of 41/84 on the leaderboard of Subtask A.
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Co-authors
- Hongfei Lin 2
- Yike Wang 1
- Liang Yang 1
- Shaowu Zhang 1
- Bingjie Yu 1
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