TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: Yes-no

PROBLEM: Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets. Further, we labeled tweets as vulgar or hate speech.  
 Question: How many tweets are in the dataset?

SOLUTION: Extractive

PROBLEM: Among all single models, LFT performs the best, followed by MinAvgOut. RL is also comparable with previous state-of-the-art models VHRED (attn) and Reranking-RL. We think that this is because LFT exerts no force in pulling the model predictions away from the ground-truth tokens, but rather just makes itself aware of how dull each response is. Consequently, its responses appear more relevant than the other two approaches. Moreover, the hybrid model (last row) outperforms all other models by a large margin.  
 Question: Which one of the four proposed models performed best?

SOLUTION:
Abstractive