In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Input: Consider Input: To collect article-level labels, we utilized a platform in the company that has been used by the market research team to collect surveys from the subscribers of different news publishers. The survey works as follows: The user is first presented with a set of selected pages (usually 4 pages and around 20 articles) from the print paper the day before. The user can select an article each time that he or she has read, and answer some questions about it. We added 3 questions to the existing survey that asked the level of partisanship, the polarity of partisanship, and which pro- or anti- entities the article presents. We also asked the political standpoint of the user. The complete survey can be found in Appendices. 
 Question: Did they crowdsource the annotations?

Output: Yes


Input: Consider Input: We use the VisDial v1.0 BIBREF0 dataset to train our models, where one example has an image with its caption, 9 question-answer pairs, and follow-up questions and candidate answers for each round. At round $r$, the caption and the previous question-answer pairs become conversational context. The whole dataset is split into 123,287/2,000/8,000 images for train/validation/test, respectively. Unlike the images in the train and validation sets, the images in the test set have only one follow-up question and candidate answers and their corresponding conversational context. 
 Question: How big is dataset for this challenge?

Output: 133,287 images


Input: Consider Input: Our contribution leverages pre-trained word embeddings (GloVe, trained on wikipedia+gigaword corpus), the DepecheMood affective lexicon, and convolutional neural networks. 
 Question: What embeddings do they use?


Output: GloVe