In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Q: We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations.
Question: What unimodal detection models were used?

A: single layer LSTM with a 150-dimensional hidden state for hate / not hate classification
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Q: For English dataset, we crawl 687 cybersecurity articles from a collection of advanced persistent threats (APT) reports which are published from 2008 to 2018.
Question: Where are the cybersecurity articles used in the model sourced from?

A: from a collection of advanced persistent threats (APT) reports which are published from 2008 to 2018
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Q: 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.
Question: How does car speak pertains to a car's physical attributes?

A:
we do not know exactly
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