You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
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.

We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties.
Question: How did they obtain the dataset?
Solution: Extractive
Why? The answer to this question has been explicitly mentioned in the context, so the question is extractive.

New input: Co-Reference Resolution: To support multi-turn interactions, it is sometimes necessary to use co-reference resolution techniques for effective retrieval. In Macaw, we identify all the co-references from the last request of user to the conversation history. The same co-reference resolution outputs can be used for different query generation components. This can be a generic or action-specific component.

Query Generation: This component generates a query based on the past user-system interactions. The query generation component may take advantage of co-reference resolution for query expansion or re-writing.

Retrieval Model: This is the core ranking component that retrieves documents or passages from a large collection. Macaw can retrieve documents from an arbitrary document collection using the Indri python interface BIBREF9, BIBREF10. We also provide the support for web search using the Bing Web Search API. Macaw also allows multi-stage document re-ranking.

Result Generation: The retrieved documents can be too long to be presented using some interfaces. Result generation is basically a post-processing step ran on the retrieved result list. In case of question answering, it can employ answer selection or generation techniques, such as machine reading comprehension models. For example, Macaw features the DrQA model BIBREF11 for question answering.
Question: What functionality does Macaw provide?
Solution:
Extractive