ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning

25 Feb 2021 β€’ Xin Xie β€’ Xiangnan Chen β€’ Xiang Chen β€’ Yong Wang β€’ Ningyu Zhang β€’ Shumin Deng β€’ Huajun Chen

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... (read more)

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Datasets


Results from the Paper


 Ranked #1 on Reading Comprehension on ReCAM (using extra training data)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
RESULT BENCHMARK
Reading Comprehension ReCAM NAL Accuracy 87.9/92.8 # 1

Methods used in the Paper


METHOD TYPE
πŸ€– No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet