TASK DEFINITION: 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.
PROBLEM: In our experiments, we used WordNet 3.0 BIBREF9 as our external knowledge base INLINEFORM0 . For word embeddings, we experimented with two popular models: (1) GloVe embeddings trained by BIBREF10 on Wikipedia and Gigaword 5 (vocab: 400K, dim: 300), and (2) w2v-gn, Word2vec BIBREF5 trained on the Google News dataset (vocab: 3M, dim: 300). Our coverage enhancement starts by transforming the knowledge base INLINEFORM0 into a vector space representation that is comparable to that of the corpus-based space INLINEFORM1 . To this end, we use two techniques for learning low-dimensional feature spaces from knowledge graphs: DeepWalk and node2vec. DeepWalk uses a stream of short random walks in order to extract local information for a node from the graph. By treating these walks as short sentences and phrases in a special language, the approach learns latent representations for each node. Similarly, node2vec learns a mapping of nodes to continuous vectors that maximizes the likelihood of preserving network neighborhoods of nodes. Thanks to a flexible objective that is not tied to a particular sampling strategy, node2vec reports improvements over DeepWalk on multiple classification and link prediction datasets. For both these systems we used the default parameters and set the dimensionality of output representation to 100. Also, note than nodes in the semantic graph of WordNet represent synsets. Hence, a polysemous word would correspond to multiple nodes. In our experiments, we use the MaxSim assumption of BIBREF11 in order to map words to synsets. 
 Question: What other embedding models are tested?

SOLUTION: GloVe embeddings trained by BIBREF10 on Wikipedia and Gigaword 5 (vocab: 400K, dim: 300) w2v-gn, Word2vec BIBREF5 trained on the Google News dataset (vocab: 3M, dim: 300) DeepWalk  node2vec

PROBLEM: 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?

SOLUTION: 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

PROBLEM: As discussed in Section SECREF2 , a variety of features that measure salience of an entity in text are available from the NLP community. We reimplemented the ones in Dunietz and Gillick BIBREF11 . This includes a variety of features, e.g. positional features, occurrence frequency and the internal POS structure of the entity and the sentence it occurs in.  
 Question: What features are used to represent the salience and relative authority of entities?

SOLUTION:
Salience features positional features, occurrence frequency and the internal POS structure of the entity and the sentence it occurs in.
The relative authority of entity features:   comparative relevance of the news article to the different entities occurring in it.