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.

This action space is of the order of $A=\mathcal {O}(|V| \times |O|^2)$ where $V$ is the number of action verbs, and $O$ is the number of distinct objects in the world that the agent can interact with. As this is too large a space for a RL agent to effectively explore, the knowledge graph is used to prune this space by ranking actions based on their presence in the current knowledge graph and the relations between the objects in the graph as in BIBREF7 
 Question: How is the domain knowledge transfer represented as knowledge graph?
the knowledge graph is used to prune this space by ranking actions based on their presence in the current knowledge graph and the relations between the objects in the graph as in BIBREF7

To increase the variety of corpus, we selected 4 English corpora and 4 Mandarin corpora in addition to the low resource language corpora. 
 Question: Do they test their approach on large-resource tasks?
Yes

Our RoBERTa baseline performs quite well on HotpotQA (77.0 F1), despite processing each paragraph separately, which prohibits inter-paragraph reasoning. We fine-tune a pre-trained model to take a question and several paragraphs and predicts the answer, similar to the single-hop QA model from BIBREF21.  We use $\textsc {RoBERTa}_{\textsc {LARGE}}$ BIBREF24 as our pre-trained initialization.  
 Question: What is the strong baseline that this work outperforms?

RoBERTa baseline