CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations

Voice@SRIB at SemEval-2020 Task 9 and 12: Stacked Ensembling method for Sentiment and Offensiveness detection in Social Media

In social-media platforms such as Twitter, Facebook, and Reddit, people prefer to use code-mixed language such as Spanish-English, Hindi-English to express their opinions. In this paper, we describe different models we used, using the external dataset to train embeddings, ensembling methods for Sentimix, and OffensEval tasks. The use of pre-trained embeddings usually helps in multiple tasks such as sentence classification, and machine translation. In this experiment, we haveused our trained code-mixed embeddings and twitter pre-trained embeddings to SemEval tasks. We evaluate our models on mac…

MIPT-NSU-UTMN at SemEval-2021 Task 5: Ensembling Learning with Pre-trained Language Models for Toxic Spans Detection

This paper describes our system for SemEval-2021 Task 5 on Toxic Spans Detection. We developed ensemble models using BERT-based neural architectures and post-processing to combine tokens into spans. We evaluated several pre-trained language models using various ensemble techniques for toxic span identification and achieved sizable improvements over our baseline fine-tuned BERT models. Finally, our system obtained a F1-score of 67.55% on test data.

YEZE在SemEval-2026任务9：通过异构集成检测多语言、多文化及多事件在线极化

YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling

本文提出了一种系统，用于SemEval-2026任务9，通过三个子任务检测22种语言的极化社交媒体内容，采用异构集成多语言预训练模型，结合多任务学习、翻译数据增强和类别加权提升分类性能。

UTSA-NLP团队在SemEval 2024任务5：基于GPT4的民事诉讼论证推理提示集成方法

Team UTSA-NLP at SemEval 2024 Task 5: Prompt Ensembling for Argument Reasoning in Civil Procedures with GPT4

本文提出基于GPT4的提示集成方法，结合思维链推理与上下文学习，用于民事诉讼法律论证推理，在SemEval 2024任务5中取得验证集Macro F1 0.8095、测试集0.7315（21队第5）的成绩。

X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications

This paper describes our winning system on SemEval 2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts. A replaced token detection pre-trained model is utilized with minorly different task-specific heads for SubTask-A: Multi-class Classification and SubTask-B: Ranking. Incorporating a pattern-aware ensemble method, our system achieves a 68.90% accuracy score and 0.8070 spearman's rank correlation score surpassing the 2nd place with a large margin by 2.7 and 2.2 percent points for SubTask-A and SubTask-B, respectively. Our approach is …

Adam-Smith在SemEval-2023任务4：基于Transformer模型集成发现论证中的人类价值观

Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models

本文提出Adam Smith方法，通过集成Transformer模型并优化全局决策阈值，在SemEval-2023任务4中识别论证中的人类价值观，取得最佳性能，并证明系统可大幅精简。