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.

The dataset contains a total of 9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words. 
 Question: What is the size of this dataset?
9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words

We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edges—these may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges). 
 Question: Did they use a relation extraction method to construct the edges in the graph?
No

While $\beta $-VAE offers regularizing the ELBO via an additional coefficient $\beta \in {\rm I\!R}^+$, a simple extension BIBREF16 of its objective function incorporates an additional hyperparameter $C$ to explicitly control the magnitude of the KL term,

where $C\!\! \in \!\! {\rm I\!R}^+$ and $| . |$ denotes the absolute value. 
 Question: How does explicit constraint on the KL divergence term that authors propose looks like?

Answer with content missing: (Formula 2) Formula 2 is an answer: 
\big \langle\! \log p_\theta({x}|{z}) \big \rangle_{q_\phi({z}|{x})}  -  \beta |D_{KL}\big(q_\phi({z}|{x}) || p({z})\big)-C|