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

Basafa, Hossein; Movahedi, Sajad; Ebrahimi, Ali; Shakery, Azadeh; Faili, Heshaam

doi:10.18653/v1/2021.semeval-1.23

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Cited by 2 publications

References 11 publications

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Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda

Kimera,

Rim,

Kirabira

et al. 2023

2023 14th International Conference on Information and Communication Technology Convergence (ICTC)

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Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda

Kimera,

Rim,

Kirabira

et al. 2023

2023 14th International Conference on Information and Communication Technology Convergence (ICTC)

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A Multiple-Choice Machine Reading Comprehension Model with Multi-Granularity Semantic Reasoning

Dai

Yang

2021

Applied Sciences

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To address the problem of poor semantic reasoning of models in multiple-choice Chinese machine reading comprehension (MRC), this paper proposes an MRC model incorporating multi-granularity semantic reasoning. In this work, we firstly encode articles, questions and candidates to extract global reasoning information; secondly, we use multiple convolution kernels of different sizes to convolve and maximize pooling of the BERT-encoded articles, questions and candidates to extract local semantic reasoning information of different granularities; we then fuse the global information with the local multi-granularity information and use it to make an answer selection. The proposed model can combine the learned multi-granularity semantic information for reasoning, solving the problem of poor semantic reasoning ability of the model, and thus can improve the reasoning ability of machine reading comprehension. The experiments show that the proposed model achieves better performance on the C3 dataset than the benchmark model in semantic reasoning, which verifies the effectiveness of the proposed model in semantic reasoning.

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

Cited by 2 publications

References 11 publications

Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda

Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda

A Multiple-Choice Machine Reading Comprehension Model with Multi-Granularity Semantic Reasoning

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