In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

Q: The task presented here was easy and simple to analyze, however, future work may be done on natural language tasks. If these properties hold it might indicate that a new evaluation paradigm for NLP should be pushed; one that emphasizes performance on uncharacteristic (but structurally sound) inputs in addition to the data typically seen in training. 
 Question: Can the findings of this paper be generalized to a general-purpose task?

A: Yes-no
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Q: We compare with the following baselines:

(1) Naive: A non-domain-adaptive baseline with bag-of-words representations and SVM classifier trained on the source domain.

(2) mSDA BIBREF7 : This is the state-of-the-art method based on discrete input features. Top 1000 bag-of-words features are kept as pivot features. We set the number of stacked layers to 3 and the corruption probability to 0.5.

(3) NaiveNN: This is a non-domain-adaptive CNN trained on source domain, which is a variant of our model by setting INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 to zeros.

(4) AuxNN BIBREF4 : This is a neural model that exploits auxiliary tasks, which has achieved state-of-the-art results on cross-domain sentiment classification. The sentence encoder used in this model is the same as ours.

(5) ADAN BIBREF16 : This method exploits adversarial training to reduce representation difference between domains. The original paper uses a simple feedforward network as encoder. For fair comparison, we replace it with our CNN-based encoder. We train 5 iterations on the discriminator per iteration on the encoder and sentiment classifier as suggested in their paper.

(6) MMD: MMD has been widely used for minimizing domain discrepancy on images. In those works BIBREF9 , BIBREF13 , variants of deep CNNs are used for encoding images and the MMDs of multiple layers are jointly minimized. In NLP, adding more layers of CNNs may not be very helpful and thus those models from image-related tasks can not be directly applied to our problem. To compare with MMD-based method, we train a model that jointly minimize the classification loss INLINEFORM0 on the source domain and MMD between INLINEFORM1 and INLINEFORM2 . For computing MMD, we use a Gaussian RBF which is a common choice for characteristic kernel. 
 Question: What are the baseline methods?

A: Extractive
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Q: 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. 
 Question: How do the authors show that their learned policy generalize better than existing solutions to unseen games?

A:
Extractive
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