instruction:
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.
question:
The results are shown in Fig. FIGREF87 for different numbers of topics, where we can see that the proposed model outperforms all the baselines, being the svi version the one that performs best. In order to assess the computational advantages of the stochastic variational inference (svi) over the batch algorithm, the log marginal likelihood (or log evidence) was plotted against the number of iterations. Fig. FIGREF88 shows this comparison. Not surprisingly, the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm. 
 Question: what are the advantages of the proposed model?
answer:
he proposed model outperforms all the baselines, being the svi version the one that performs best. the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm.


question:
The grammar induction method works on the premise of curriculum learning BIBREF7 , where the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns. A context-free grammar (CFG) is induced from the text, which is represented by several layers of semantic annotations. 
 Question: How did they induce the CFG?
answer:
the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns


question:
BIBREF6 introduce the KG-A2C, which uses a knowledge graph based state-representation to aid in the section of actions in a combinatorially-sized action-space—specifically they use the knowledge graph to constrain the kinds of entities that can be filled in the blanks in the template action-space. They test their approach on Zork1, showing the combination of the knowledge graph and template action selection resulted in improvements over existing methods. They note that their approach reaches a score of 40 which corresponds to a bottleneck in Zork1 where the player is eaten by a “grue” (resulting in negative reward) if the player has not first lit a lamp. 
 Question: What are the baselines?
answer:

a score of 40