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.

Ex Input:
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.

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


Ex Input:
Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier.

Ex Output:
what types of features were used?


Ex Input:
The corpus is freely available at the following link

Ex Output:

Is this dataset publicly available?