instruction:
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.
question:
Car-speak is abstract language that pertains to a car's physical attribute(s). In this instance the physical attributes that the term "fast" pertains to could be the horsepower, or it could be the car's form factor (how the car looks). However, we do not know exactly which attributes the term "fast" refers to.
answer:
How does car speak pertains to a car's physical attributes?

question:
The improvement when the difficult subset with expert annotations is mixed with the remaining crowd annotation is 3.5 F1 score, much larger than when a random set of expert annotations are added.
answer:
How much higher quality is the resulting annotated data?

question:
There are three main components to the model: 1) input encoder 2) dynamic memory, and 3) output module. We will describe these three modules in details. The input encoder and output module implementations are similar to the Entity Network BIBREF17 and main novelty lies in the dynamic memory. We describe the operations executed by the network for a single example consisting of a document with $T$ sentences, where each sentence consists of a sequence of words represented with $K$ -dimensional word embeddings $\lbrace e_1, \ldots , e_N
brace $ , a question on the document represented as another sequence of words and an answer to the question.
answer:
How is knowledge stored in the memory?