In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Input: Consider Input: Our corpus comprises a total of 6,127 scientific entities, including 2,112 Process, 258 Method, 2,099 Material, and 1,658 Data entities. 

Output: How large is the dataset?


Input: Consider Input: Attention-base translation model We use the system of BIBREF6 , a convolutional sequence to sequence model.

Output: Which translation system do they use to translate to English?


Input: Consider Input: We further use as features the embeddings of the claim, of the best-scoring snippet, and of the best-scoring sentence triplet from a Web page. We calculate these embeddings (i) as the average of the embeddings of the words in the text, and also (ii) using LSTM encodings, which we train for the task as part of a deep neural network (NN). We also use a task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN.


Output: What algorithm and embedding dimensions are used to build the task-specific embeddings?