In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[EX Q]: Here I would use Informap algorithm BIBREF12. To make a comparison to this method, I am using CCM and SCA for distance measurement in this experiment, too. UPGMA algorithm would be used accordingly in these two cases.
[EX A]: Is the proposed method compared to previous methods?

[EX Q]: In particular, we investigated attention to nouns, verbs, pronouns, subjects, objects, and negation words, and special BERT tokens across the tasks.
[EX A]: What handcrafter features-of-interest are used?

[EX Q]: As shown in Figure FIGREF7 , KAR is an end-to-end MRC model consisting of five layers:

Lexicon Embedding Layer. This layer maps the words to the lexicon embeddings. Context Embedding Layer. This layer maps the lexicon embeddings to the context embeddings. Coarse Memory Layer. This layer maps the context embeddings to the coarse memories. Refined Memory Layer. This layer maps the coarse memories to the refined memories. Answer Span Prediction Layer. This layer predicts the answer start position and the answer end position based on the above layers.
[EX A]:
What type of model is KAR?