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 SG model achieved a high average similarity score of 0.650 followed by CBoW with a 0.632 average similarity score. The GloVe also achieved a considerable average score of 0.591 respectively. However, the average similarity score of SdfastText is 0.388 and the word pair Microsoft-Bill Gates is not available in the vocabulary of SdfastText. Moreover, the average semantic relatedness similarity score between countries and their capitals is shown in Table TABREF78 with English translation, where SG also yields the best average score of 0.663 followed by CBoW with 0.611 similarity score. The GloVe also yields better semantic relatedness of 0.576 and the SdfastText yield an average score of 0.391. 
 Question: How does proposed word embeddings compare to Sindhi fastText word representations?
[A]: Abstractive


[Q]: Following the formula, we can calculate the contribution of every input word makes to every output word, forming a contribution matrix of size $M \times N$, where $N$ is the output sentence length. Given the contribution matrix, we can obtain the word importance of each input word to the entire output sentence. To this end, for each input word, we first aggregate its contribution values to all output words by the sum operation, and then normalize all sums through the Softmax function. 
 Question: How do their models decide how much improtance to give to the output words?
[A]: Extractive


[Q]: We therefore use reduction in softmax probability of the correct relation as our signaling strength metric for the model. We call this metric ${\Delta }_s$ (for delta-softmax), which can be written as:

where $rel$ is the true relation of the EDU pair, $t_i$ represents the token at index $i$ of $N$ tokens, and $X_{mask=i}$ represents the input sequence with the masked position $i$ (for $i \in 1 \ldots N$ ignoring separators, or $\phi $, the empty set). 
 Question: How is the delta-softmax calculated?
[A]:
Abstractive