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:
In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST). Denoting a feature as such with INLINEFORM4 , we construct the pooling vector INLINEFORM5 for the char-bigrams, and concatenate INLINEFORM6 to INLINEFORM7 before feeding the resulting vector to the softmax layer.

Ex Output:
How are discourse features incorporated into the model?

Ex Input:
In this approach the similarity between two words is not strictly based on their cooccurrence frequencies, but rather on the frequencies of the other words which occur with both of them (i.e., second order cooccurrences). This approach has been shown to be successful in quantifying semantic relatedness BIBREF12 , BIBREF13 .

Ex Output:
What is a second order co-ocurrence matrix?

Ex Input:
Our features consisted of direct similarity over ConceptNet Numberbatch embeddings, the relationships inferred over ConceptNet by SME, features that compose ConceptNet with other resources (WordNet and Wikipedia), and a purely corpus-based feature that looks up two-word phrases in the Google Books dataset.

Ex Output:
What features did they train on?