In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

[Q]: 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.
Question: How are discourse features incorporated into the model?
[A]: Abstractive

[Q]: Comparing the natural and artificial sources of our parallel data wrt. several linguistic and distributional properties, we observe that (see Fig. FIGREF21 - FIGREF22 ):

artificial sources are on average shorter than natural ones: when using BT, cases where the source is shorter than the target are rarer; cases when they have the same length are more frequent.

automatic word alignments between artificial sources tend to be more monotonic than when using natural sources, as measured by the average Kendall INLINEFORM0 of source-target alignments BIBREF22 : for French-English the respective numbers are 0.048 (natural) and 0.018 (artificial); for German-English 0.068 and 0.053. The intuition is that properties (i) and (ii) should help translation as compared to natural source, while property (iv) should be detrimental.
Question: what is their explanation for the effectiveness of back-translation?
[A]: Extractive

[Q]: A dialog turn from one speaker may not only be a direct response to the other speaker's query, but also likely to be a continuation of his own previous statement. Thus, when modeling turn $k$ in a dialog, we propose to connect the last RNN state of turn $k-2$ directly to the starting RNN state of turn $k$ , instead of letting it to propagate through the RNN for turn $k-1$ .
Question: How long of dialog history is captured?
[A]:
Abstractive