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.

Example Input: Maximum matching (MM) is one of the most popular fundamental and structural segmentation algorithms for word segmentation BIBREF19 .  However, it does not solve the problem of ambiguous words and unknown words that do not exist in the dictionary. 
 Question: What are the limitations of existing Vietnamese word segmentation systems?
Example Output: Extractive

Example 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. 
 Question: How are discourse features incorporated into the model?
Example Output: Abstractive

Example Input: We explore how to utilize additional commonsense knowledge (i.e. rationales) as the input to the task. Like we mentioned in Section SECREF6, we search relevant sentences from the OMCS corpus as the additional distant rationales, and ground truth rationale sentences for dev/test data. The inputs are no longer the concept-sets themselves, but in a form of “[rationales$|$concept-set]” (i.e. concatenating the rationale sentences and original concept-set strings). 
 Question: Are the models required to also generate rationales?
Example Output:
Yes-no