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.

They annotated 7.8K verbs, reporting an average of 2.4 QA pairs per predicate. Even though multiple annotators were shown to produce greater coverage, their released dataset was produced using only a single annotator per verb. 
 Question: How was coverage measured?
QA pairs per predicate

We model interactions between observations from the environment, goal, and document using layers. We first encode text inputs using bidirectional LSTMs, then compute summaries using self-attention and conditional summaries using attention. We concatenate text summaries into text features, which, along with visual features, are processed through consecutive layers. In this case of a textual environment, we consider the grid of word embeddings as the visual features for . The final output is further processed by MLPs to compute a policy distribution over actions and a baseline for advantage estimation.  
 Question: How does propose model model that capture three-way interactions?
 We first encode text inputs using bidirectional LSTMs, then compute summaries using self-attention and conditional summaries using attention. We concatenate text summaries into text features, which, along with visual features, are processed through consecutive layers. In this case of a textual environment, we consider the grid of word embeddings as the visual features for . The final output is further processed by MLPs to compute a policy distribution over actions and a baseline for advantage estimation.

Evaluating FastText, GloVe and word2vec, we show that compared to other word representation learning algorithms, the FastText performs best. 
 Question: What do you use to calculate word/sub-word embeddings

FastText