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]: We evaluate the quality of the document embeddings learned by the different variants of CAHAN and the HAN baseline on three of the large-scale document classification datasets introduced by BIBREF14 and used in the original HAN paper BIBREF5. 
 Question: Do they compare to other models appart from HAN?
[A]: Yes-no


[Q]: The result on original WikiQA indicates that all three transfer learning methods not only do not improve the results but also hurt the F1-score. These are because other datasets could not add new information to the original dataset or they apparently add some redundant information which are dissimilar to the target dataset. 
 Question: Do transferring hurt the performance is the corpora are not related?
[A]: Yes-no


[Q]: Our second approach incorporates shallow syntactic information in downstream tasks via token-level chunk label embeddings. Task training (and test) data is automatically chunked, and chunk boundary information is passed into the task model via BIOUL encoding of the labels. We add randomly initialized chunk label embeddings to task-specific input encoders, which are then fine-tuned for task-specific objectives. 
 Question: Which syntactic features are obtained automatically on downstream task data?
[A]:
Extractive