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.
One example is below.
Q: 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?
A: Extractive
Rationale: The answer to this question has been explicitly mentioned in the context, so the question is extractive.
Q: Below, we explore different ways to back off when the ScRNN predicts UNK (a frequent outcome for rare and unseen words):

Pass-through: word-recognizer passes on the (possibly misspelled) word as is.

Backoff to neutral word: Alternatively, noting that passing $\colorbox {gray!20}{ exttt {UNK}}$ -predicted words through unchanged exposes the downstream model to potentially corrupted text, we consider backing off to a neutral word like 'a', which has a similar distribution across classes.

Backoff to background model: We also consider falling back upon a more generic word recognition model trained upon a larger, less-specialized corpus whenever the foreground word recognition model predicts UNK. Figure 1 depicts this scenario pictorially.
Question: How do the backoff strategies work?
A:
Abstractive