Title

Attention based transformer for student answers assessment

Abstract

Inspired by Vaswani's transformer, we propose in this paper an attention-based transformer neural network with a multi-head attention mechanism for the task of student answer assessment. Results show the competitiveness of our proposed model. A highest accuracy of 71.5% was achieved when using ELMo embeddings, 10 heads of attention, and 2 layers. This is very competitive and rivals the highest accuracy achieved by a previously proposed BI-GRU-Capsnet deep network (72.5%) on the same dataset. The main advantages of using transformers over BI-GRU-Capsnet is reducing the training time and giving more space for parallelization.

Publication Title

Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020

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