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.
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Question: The challenge is addressed as follows: given a natural language input sequence describing the scene, such as a piece of a story coming from a transcript, the goal is to infer which action is most likely to happen next. 
 Question: Do they literally just treat this as "predict the next spell that appears in the text"?

Answer: Yes-no


Question: The annotator carried out all annotation. 
 Question: How many annotators tagged each tweet?

Answer: Abstractive


Question: However, a significant bottleneck that has been overlooked by previous efforts is the input vocabulary size and its corresponding word embedding matrix, often accounting for a significant proportion of all model parameters. For instance, the embedding table of the BERTBASE model, comprising over 30K WordPiece tokens BIBREF14, accounts for over $21\%$ of the model size. While there has been existing work on reducing NLP model vocabulary sizes BIBREF15, distillation techniques cannot utilize these, since they require the student and teacher models to share the same vocabulary and output space. This profoundly limits their potential to further reduce model sizes. 
 Question: Why are prior knowledge distillation techniques models are ineffective in producing student models with vocabularies different from the original teacher models?  

Answer:
Extractive