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 consider two types of architectures: a bi-directional language model to augment the sequence to sequence encoder and a uni-directional model to augment the decoder. Both use self-attention BIBREF16 and the uni-directional model contains INLINEFORM0 transformer blocks, followed by a word classifier to predict the next word on the right. bi-directional language model to augment the sequence to sequence encoder 
 Question: What language model architectures are used?

A: Extractive
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Q: TABREF5 displays the output behavior of models that we damaged to resemble the damage that causes aphasia. Because the output languages in all of our domains use tokens to represent meanings in many cases, it is expected that the analog to Wernicke's area is responsible for maintaining a high precision. 
 Question: Do they perform a quantitative analysis of their model displaying knowledge distortions?

A: Yes-no
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Q: In this study, we investigate the efficacy of bias reduction during training by introducing a new loss function which encourages the language model to equalize the probabilities of predicting gendered word pairs like he and she.  
 Question: what kinds of male and female words are looked at?

A:
Extractive
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