Educational Multi-Question Generation for Reading Comprehension

Automated scoring technology for short-answer questions has been attracting attention to improve the fairness of scoring and reduce the burden on the scorer. In general, a large amount of data is required to train an automated scoring model. The training data consists of the answer texts and the scoring data assigned to them. It may also include annotations indicating key word sequences. Many previous studies have created models with large amounts of training data specific to each question. This paper aims to achieve equivalent performance with less training data by utilizing a BERT model that has been pre-trained on a large amount of general text data not necessarily related to short answer questions. On the RIKEN dataset, the proposed method reduces the training data from the 800 data required in the past to about 400 data, and still achieves scoring accuracy comparable to that of humans. Annotating 400 data is still costly, but it is beneficial to reduce the number of data needed.

Semantic Diversity in Dialogue with Natural Language Inference

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W12: BEA-2022: 17th Workshop on Innovative Use of NLP for Building Educational Applications

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## Welcome to NAACL 2022!
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Educational Multi-Question Generation for Reading Comprehension

Semantic Diversity in Dialogue with Natural Language Inference

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