instruction:
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.
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:
To capture this interesting property, we propose a new tagging scheme consisting of three tags, namely { INLINEFORM0 }.

INLINEFORM0 tag indicates that the current word appears before the pun in the given context.

INLINEFORM0 tag highlights the current word is a pun.

INLINEFORM0 tag indicates that the current word appears after the pun.
answer:
What is the tagging scheme employed?


question:
Modules: Modules are the basic building blocks of different models. In LeafNATS, we provide ready-to-use modules for constructing recurrent neural network (RNN)-based sequence-to-sequence (Seq2Seq) models for NATS, e.g., pointer-generator network BIBREF1 . These modules include embedder, RNN encoder, attention BIBREF24 , temporal attention BIBREF6 , attention on decoder BIBREF2 and others. We also use these basic modules to assemble a pointer-generator decoder module and the corresponding beam search algorithms. The embedder can also be used to realize the embedding-weights sharing mechanism BIBREF2 .
answer:

What models are included in the toolkit?