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.

[EX Q]: the attention heads in the proposed adaptively sparse Transformer can specialize more and with higher confidence In particular, we analyzed how the attention heads in the proposed adaptively sparse Transformer can specialize more and with higher confidence. 
 Question: How does their model improve interpretability compared to softmax transformers?
[EX A]: Extractive

[EX Q]: In addition, we evaluate the effectiveness of pre-training by comparing it with a jointly-trained model of forward translation and back-translation. Experimental results show that the encoder-decoder-reconstructor offers significant improvement in BLEU scores and alleviates the problem of repeating and missing words in the translation on English-Japanese translation task, and the encoder-decoder-reconstructor can not be trained well without pre-training, so it proves that we have to train the forward translation model in a manner similar to the conventional attention-based NMT as pre-training. 
 Question: Is pre-training effective in their evaluation?
[EX A]: Yes-no

[EX Q]: Each classifier is implemented with the following specifications:

Naïve Bayes (NB): Multinomial NB with additive smoothing constant 1

Logistic Regression (LR): Linear LR with L2 regularization constant 1 and limited-memory BFGS optimization

Support Vector Machine (SVM): Linear SVM with L2 regularization constant 1 and logistic loss function

Random Forests (RF): Averaging probabilistic predictions of 10 randomized decision trees

Gradient Boosted Trees (GBT): Tree boosting with learning rate 1 and logistic loss function Along with traditional machine learning approaches, we investigate neural network based models to evaluate their efficacy within a larger dataset. In particular, we explore Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and their variant models. 
 Question: What learning models are used on the dataset?
[EX A]:
Extractive