BERT-based Automatic Text Scoring for Collaborative Learning

Fukuda, H.; Tsunakawa, Takashi; Oshima, Jun; Oshima, Ritsuko; Nishida, Masafumi; Nishimura, Masafumi

doi:10.1109/gcce50665.2020.9291880

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“…Whereas, the second model aims at accommodating the desired task such as question answer, document classification or ranking. However, multiple recent researches showed that BERT architecture has non-outstanding performance on AES task compared to techniques ( [16]; [30]; [43]). Although BERT showed magnificent performance in problems like question answering, its architecture failed to give an accurate scoring for an answer.…”

Section: F Summary Of Related Workmentioning

confidence: 99%