In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
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Question: On the other hand, Go-Explore Seq2Seq shows promising results by solving almost half of the unseen games. Figure FIGREF62 (in Appendix SECREF60) shows that most of the lost games are in the hardest set, where a very long sequence of actions is required for winning the game. These results demonstrate both the relative effectiveness of training a Seq2Seq model on Go-Explore trajectories, but they also indicate that additional effort needed for designing reinforcement learning algorithms that effectively generalize to unseen games.

Answer: How do the authors show that their learned policy generalize better than existing solutions to unseen games?


Question: Because our approach is specifically intended to yield sentences that are grammatical, we additionally consider the following log ratio (i.e., the grammatical phrase over the ungrammatical phrase):

Answer: How do they measure grammaticality?


Question: terms extracted from Yahoo! Answers tend to be more related, in terms of the number of correlated terms, to attributes related to religion or ethnicity compared to terms from Twitter. However, for two particular attributes (i.e., Price and Buddhist), the number of correlated terms from Twitter is higher than the ones from Yahoo! Answers . 

Answer:
On Twitter, do the demographic attributes and answers show more correlations than on Yahoo! Answers?