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.

Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers. 
 Question: How big is dataset of car-speak language?
$3,209$ reviews 

replacing this single GRU with two different components first component is a sentence reader second component is the input fusion layer 
 Question: What changes they did on input module?
For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader The second component is the input fusion layer

In this paper, we propose to represent each word with an expressive multimodal distribution, for multiple distinct meanings, entailment, heavy tailed uncertainty, and enhanced interpretability. For example, one mode of the word `bank' could overlap with distributions for words such as `finance' and `money', and another mode could overlap with the distributions for `river' and `creek'. It is our contention that such flexibility is critical for both qualitatively learning about the meanings of words, and for optimal performance on many predictive tasks. 
 Question: How does this compare to contextual embedding methods?

 represent each word with an expressive multimodal distribution, for multiple distinct meanings, entailment, heavy tailed uncertainty, and enhanced interpretability. For example, one mode of the word `bank' could overlap with distributions for words such as `finance' and `money', and another mode could overlap with the distributions for `river' and `creek'.