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.

Ex Input:
We only used models of dimension 300 and, for Word2Vec, Wang2Vec, and FastText, skip-gram architectured models.
Question: what word embedding techniques did they experiment with?

Ex Output:
Word2Vec, Wang2Vec, and FastText

Ex Input:
We combine 13k questions gathered from this pipeline with an additional 3k questions with yes/no answers from the NQ training set to reach a total of 16k questions.
Question: what is the size of BoolQ dataset?

Ex Output:
16k questions

Ex Input:
To fine-tune GPT2 for text generation, it is typical to concatenate the conditioning context $X = x_1 \ldots x_n$ and citing sentence $Y = y_1 \ldots y_m$ with a special separator token $\mho $. The abstract of each document is embedded into a single dense vector by averaging the contextualized embeddings provided by the SciBERT model of BIBREF11 and normalizing.
Question: Which baselines are explored?

Ex Output:
GPT2 SciBERT model of BIBREF11