ZJUKLAB at SemEval-2021 Task 4：Negative Augmentation with语言模型for Reading Comprehension of Abstract Meaning
ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning
Zhejiang University & AZFT Joint Lab for Knowledge Engine（浙江大学与AZFT联合知识引擎实验室）；Hangzhou Innovation Center, Zhejiang University（浙江大学杭州创新中心）；Microsoft（微软）
This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively. We further conduct comprehensive model analysis and observe interesting error cases, which may promote future researches.
HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension
Joint Laboratory of HIT and iFLYTEK (HFL), iFLYTEK Research（哈尔滨工业大学与科大讯飞联合实验室（HFL）、科大讯飞研究院）；Research Center for Social Computing and Information Retrieval (SCIR),Harbin Institute of Technology（哈尔滨工业大学社会计算与信息检索研究中心（SCIR））；Harbin Institute of Technology（哈尔滨工业大学）；iFLYTEK Research（科大讯飞研究院）
ReCAM@IITK at SemEval-2021 Task 4: BERT and ALBERT based Ensemble for Abstract Word Prediction
Indian Institute of Technology Kanpur（坎普尔印度理工学院）
本文介绍了我们的系统SemEval-2021的任务4：阅读理解的抽象意义（ReCAM）。我们参与了所有的子任务，主要目标是预测一个陈述中缺少的抽象词。我们微调了预先训练的掩蔽语言模型，即BERT和AL BERT，并将其作为我们在子任务1（ReCAM-不可感知性）和子任务2（ReCAM-非特异性）上提交的系统。对于子任务3（ReCAM-Intersection），我们提交了ALBERT模型，因为它给出了最好的结果。我们尝试了多种方法，发现基于Masked Language Modeling（MLM）的方法效果最好。
PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection
Ping An Life Insurance Company of China, Ltd.（中国平安人寿保险股份有限公司）
Patronizing and Condensing Language（PCL）具有很大的有害影响，并且对于人类法官和现有的NLP系统都难以检测。在SemEval-2022任务4中，我们提出了一种新的基于transformer的模型及其集成，以准确理解PCL检测的语言上下文。为了促进对PCL的微妙和主观性质的理解，两个微调策略被应用于捕捉不同的语言行为和分类分布的歧视性特征。该系统在官方排名中取得了显著的成绩，包括子任务1的第一名和子任务2的第五名。大量的实验任务证明了我们的系统和策略的有效性。
IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
