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.

Q: We evaluate the generations with the ROUGE BIBREF29 and METEOR BIBREF30 metrics using the true sentences as targets. 
 Question: What metrics are used for evaluation?

A: ROUGE BIBREF29 and METEOR BIBREF30
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Q: We compare our model with two sets of baselines:

MemN2N BIBREF12 is an end-to-end trainable version of memory networks BIBREF9 . Attentive and Impatient Readers BIBREF6 use bidirectional LSTMs to encode question and evidence, and do classification over a large vocabulary based on these two encodings. 
 Question: What are the baselines?

A: MemN2N BIBREF12 Attentive and Impatient Readers BIBREF6
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Q: We first measure the utility of various components in Logician to select the optimal model, and then compare this model to the state-of-the-art methods in four types of information extraction tasks: verb/preposition-based relation, nominal attribute, descriptive phrase and hyponymy relation. 
 Question: What open relation extraction tasks did they experiment on?

A:
verb/preposition-based relation, nominal attribute, descriptive phrase and hyponymy relation.
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