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task ID: readingcomprehensionofabstractmeaning

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LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractWe present our approaches and methods for SemEval-2021 Task-4 Reading Comprehension of Abstract Meaning. Given a question with a fill-in-the-blank, and a corresponding context, the task is to predict the …...

25749

NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document Transformer

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Publication: TIPSTER (2021)

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AbstractThis paper presents a technical report of our submission to the 4th task of SemEval-2021, titled: Reading Comprehension of Abstract Meaning. In this task, we want to predict the correct …...

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ReCAM@IITK at SemEval-2021 Task 4: BERT and ALBERT based Ensemble for Abstract Word Prediction

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractThis paper describes our system for Task 4 of SemEval-2021: Reading Comprehension of Abstract Meaning (ReCAM). We participated in all subtasks where the main goal was to predict an abstract …...

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UoR at SemEval-2021 Task 4: Using Pre-trained BERT Token Embeddings for Question Answering of Abstract Meaning

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Publication: TIPSTER (2021)

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AbstractMost question answering tasks focuses on predicting concrete answers, e.g., named entities. These tasks can be normally achieved by understanding the contexts without additional information required. In Reading Comprehension of …...

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XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract Meaning

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractThis paper presents our submitted system to SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning. Our system uses a large pre-trained language model as the encoder and an additional …...

25563