utilize the semantic representations of text materials (document, question and options) to predict exam question difficulty (i.e. the percentage of examinees with wrong answer for a particular question). TACNN uses pre-trained word2vec embedding [@mikolov2013distributedWORD2VEC] to represent word tokens. By applying convolutional neural networks to the sequence of text tokens and attention mechanism to the series of sentences, the model quantifies the difficulty of the question.

QuesNet [@yin2019quesnet] is a question embedding model pre-trained with context information of question data. Since existing pre-training methods in NLP is not applicable to heterogeneous data in a question (such as images and metadata), the authors suggest Holed Language Model (HLM), a pre-training task in parallel to BERT’s masking language model. HLM differs to BERT’s task, however, because it predicts each input based on the values of other inputs aggregated in the Bi-LSTM layer of QuesNet, while BERT masks existing sequence randomly with certain probability. Also, QuesNet introduces another task called Domain-Oriented Objective (DOO), which is the prediction of the correctness of answer supplied with question, to capture high-level logical information. QuesNet adds two losses for HLM and DOO respectively for final loss in training. Compared to other baseline models, QuesNet shows the best performance 