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.

[EX Q]: In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints. In this work, we investigate the application of BERT-based models for the task of subjective language detection. Experiments ::: Dataset and Experimental Settings
We perform our experiments on the WNC dataset open-sourced by the authors of BIBREF2. It consists of aligned pre and post neutralized sentences made by Wikipedia editors under the neutral point of view. It contains $180k$ biased sentences, and their neutral counterparts crawled from $423,823$ Wikipedia revisions between 2004 and 2019
Question: Which experiments are perfomed?
[EX A]: Abstractive

[EX Q]: For all the tasks in our experimental study, we use 36 millions English tweets collected between August and September 2017.
Question: Do they report results only on English datasets?
[EX A]: Yes-no

[EX Q]: Paraphrases can be obtained by translating an English string into a foreign language and then back-translating it into English.
Question: It looks like learning to paraphrase questions, a neural scoring model and a answer selection model cannot be trained end-to-end. How are they trained?
[EX A]:
Abstractive