the linear map $W^O$ to aggregate each attention result.

After we compute the resulting value in Equation \[eqn:multihead\], we apply point-wise feed-forward networks to add non-linearity to the model. Also, we apply the skip connection [@he2016deep] and layer normalization [@ba2016layer] to the output of the feed-forward networks.

Assume that the last encoder block returns the series $H = [H_1,$ $ \dots, H_T]^T$ of vectors. For pre-training, the prediction to $i$’th interaction is made by applying a linear layer with softmax activation function to $H_i$. The final output is the estimated probability distribution of four possible combinations of assessment $A_t$: $(\texttt{response\_correctness}_t, \texttt{timeliness}_t) = (0, 0)$, $(0, 1)$, $(1, 0)$ or $(1, 1)$. The overall loss is defined as $$\mathcal{L} = \sum_{t=1}^{T} m_{t}\mathcal{L}_{t}$$ where $\mathcal{L}_{t}$ is the cross-entropy between estimated distribution of $A_t$ and actual one-hot distribution of $A_t$. The value $m_{t}$ is a flag that represents whether the $t$-th question is masked ($m_{t}=1$) or not ($m_{t}=0$). In other words, the total loss is the sum of cross-entropy losses for masked questions.

The input embedding layer and encoder blocks are shared over pre-training and fine-tuning. For fine-tuning, we replace the linear layers applied to each $H_i$ in pre-training by a single linear 