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


[Q]: In this section we discuss the state of the art on conversational systems in three perspectives: types of interactions, types of architecture, and types of context reasoning. ELIZA BIBREF11 was one of the first softwares created to understand natural language processing.  Right after ELIZA came PARRY, developed by Kenneth Colby, who is psychiatrist at Stanford University in the early 1970s. A.L.I.C.E. (Artificial Linguistic Internet Computer Entity) BIBREF12 appeared in 1995 but current version utilizes AIML, an XML language designed for creating stimulus-response chat robots BIBREF13 . Cleverbot (1997-2014) is a chatbot developed by the British AI scientist Rollo Carpenter.  
 Question: What is the state of the art described in the paper?
[A]: Extractive


[Q]: We introduce a surrogate training objective that avoids these problems and as a result is fully continuous.  Specifically, we form a continuous function softLB that seeks to approximate the result of running our decoder on input INLINEFORM0 and then evaluating the result against INLINEFORM1 using INLINEFORM2 . 
 Question: Do they provide a framework for building a sub-differentiable for any final loss metric?
[A]:
Yes-no