In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Ex Input:
The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061.

Ex Output:
Which dimensionality do they use for their embeddings?


Ex Input:
Attention-base translation model We use the system of BIBREF6 , a convolutional sequence to sequence model.

Ex Output:
Which translation system do they use to translate to English?


Ex Input:
TF-IDF: We applied TF-IDF features as inputs to four classifiers: support vector machine classifier (SVM), logistic regression classifier (LR), Naive Bayes classifier (NB) and random forest (RF); CNN: We followed the same architecture as BIBREF9; LSTM: We applied an LSTM model BIBREF20 to classify the sentences with pre-trained word embeddings;LSTM-soft: We then added a soft-attention BIBREF21 layer on top of the LSTM model where we computed soft alignment scores over each of the hidden states; LSTM-self: We applied a self-attention layer BIBREF22 to LSTM model. 

Ex Output:

To what baseline models is proposed model compared?