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:
As the gold standard sentiment lexica, we chose manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15 .

Ex Output:
what sentiment sources do they compare with?


Ex Input:
We adapted BERTNLU from ConvLab-2.  We implemented a rule-based model (RuleDST) and adapted TRADE (Transferable Dialogue State Generator) BIBREF19 in this experiment.  We adapted a vanilla policy trained in a supervised fashion from ConvLab-2 (SL policy). 

Ex Output:
What are the benchmark models?


Ex Input:
Logistic regression: To produce the representation of the input, we concatenate the Bag-Of-Words representation of the document with the Bag-Of-Words representation of the question. LSTM: We start with a concatenation of the sequence of indexes of the document with the sequence of indexes of the question. Them we feed an LSTM network with this vector and use the final state as the representation of the input. Finally, we apply a logistic regression over this representation to produce the final decision. End-to-end memory networks: This architecture is based on two different memory cells (input and output) that contain a representation of the document. Deep projective reader: This is a model of our own design, largely inspired by the efficient R-net reader BIBREF12 .

Ex Output:

What baselines are presented?