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: 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?
Output: 


Answer: Extractive