Definition: 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.

Positive Example 1 -
Input: We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties. 
 Question: How did they obtain the dataset?
Output: Extractive

Positive Example 2 -
Input: We evaluate our pointer-generator performance using BLEU score. The baseline language model is trained using RNNLM BIBREF23 . Perplexity measure is used in the evaluation.

 
 Question: Did they use other evaluation metrics?
Output: Yes-no

Negative Example 1 -
Input: For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  The evaluation of the German version is in progress. 
 Question: What are the corpora used for the task?
Output: Yes-no

Negative Example 2 -
Input: For Turkish, as the first dataset, we utilised the movie reviews which are collected from a popular website. The number of reviews in this movie corpus is 20,244 and the average number of words in reviews is 39. Each of these reviews has a star-rating score which is indicative of sentiment. These polarity scores are between the values 0.5 and 5, at intervals of 0.5. We consider a review to be negative it the score is equal to or lower than 2.5. On the other hand, if it is equal to or higher than 4, it is assumed to be positive. We have randomly selected 7,020 negative and 7,020 positive reviews and processed only them. 
 Question: What details are given about the movie domain dataset?
Output: Extractive

Now complete the following example -
Input: Results ::: CoinCollector
In this setting, we compare the number of actions played in the environment (frames) and the score achieved by the agent (i.e. +1 reward if the coin is collected). In Go-Explore we also count the actions used to restore the environment to a selected cell, i.e. to bring the agent to the state represented in the selected cell. This allows a one-to-one comparison of the exploration efficiency between Go-Explore and algorithms that use a count-based reward in text-based games. Importantly, BIBREF8 showed that DQN and DRQN, without such counting rewards, could never find a successful trajectory in hard games such as the ones used in our experiments. Figure FIGREF17 shows the number of interactions with the environment (frames) versus the maximum score obtained, averaged over 10 games of the same difficulty. As shown by BIBREF8, DRQN++ finds a trajectory with the maximum score faster than to DQN++. On the other hand, phase 1 of Go-Explore finds an optimal trajectory with approximately half the interactions with the environment. Moreover, the trajectory length found by Go-Explore is always optimal (i.e. 30 steps) whereas both DQN++ and DRQN++ have an average length of 38 and 42 respectively. In case of Game CoinCollector,  In CookingWorld, we compared models in the three settings mentioned earlier, namely, single, joint, and zero-shot. In all experiments, we measured the sum of the final scores of all the games and their trajectory length (number of steps). Table TABREF26 summarizes the results in these three settings. Phase 1 of Go-Explore on single games achieves a total score of 19,530 (sum over all games), which is very close to the maximum possible points (i.e. 19,882), with 47,562 steps. A winning trajectory was found in 4,279 out of the total of 4,440 games. This result confirms again that the exploration strategy of Go-Explore is effective in text-based games. Next, we evaluate the effectiveness and the generalization ability of the simple imitation learning policy trained using the extracted trajectories in phase 1 of Go-Explore in the three settings mentioned above. In this setting, each model is trained from scratch in each of the 4,440 games based on the trajectory found in phase 1 of Go-Explore (previous step). As shown in Table TABREF26, the LSTM-DQN BIBREF7, BIBREF8 approach without the use of admissible actions performs poorly. One explanation for this could be that it is difficult for this model to explore both language and game strategy at the same time; it is hard for the model to find a reward signal before it has learned to model language, since almost none of its actions will be admissible, and those reward signals are what is necessary in order to learn the language model. As we see in Table TABREF26, however, by using the admissible actions in the $\epsilon $-greedy step the score achieved by the LSTM-DQN increases dramatically (+ADM row in Table TABREF26). DRRN BIBREF10 achieves a very high score, since it explicitly learns how to rank admissible actions (i.e. a much simpler task than generating text). Finally, our approach of using a Seq2Seq model trained on the single trajectory provided by phase 1 of Go-Explore achieves the highest score among all the methods, even though we do not use admissible actions in this phase. However, in this experiment the Seq2Seq model cannot perfectly replicate the provided trajectory and the total score that it achieves is in fact 9.4% lower compared to the total score achieved by phase 1 of Go-Explore. Figure FIGREF61 (in Appendix SECREF60) shows the score breakdown for each level and model, where we can see that the gap between our model and other methods increases as the games become harder in terms of skills needed. In this setting the 4,440 games are split into training, validation, and test games. The split is done randomly but in a way that different difficulty levels (recipes 1, 2 and 3), are represented with equal ratios in all the 3 splits, i.e. stratified by difficulty. As shown in Table TABREF26, the zero-shot performance of the RL baselines is poor, which could be attributed to the same reasons why RL baselines under-perform in the Joint case. Especially interesting is that the performance of DRRN is substantially lower than that of the Go-Explore Seq2Seq model, even though the DRRN model has access to the admissible actions at test time, while the Seq2Seq model (as well as the LSTM-DQN model) has to construct actions token-by-token from the entire vocabulary of 20,000 tokens. 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 better does new approach behave than existing solutions?
Output: 


Answer: Extractive