TASK DEFINITION: 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.
PROBLEM: The resulting dataset consists of 22,880 users, 41,094 blogs, and 561,003 posts. Table TABREF2 presents additional statistics of our dataset.

SOLUTION: How many users do they look at?

PROBLEM: Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings.

SOLUTION: How big is improvement over the old  state-of-the-art performance on CoNLL-2009 dataset?

PROBLEM: The learning model for retrieval is trained by an oracle constructed using distant supervision. Using the answer labels in the training set, we can find appropriate articles that include the information requested in the question. First, the question and article are embedded into vector sequences, using the same method as the comprehension model. We do not use anonymization here, to retain simplicity. Otherwise, the anonymization procedure would have to be repeated several times for a potentially large collection of documents. These vector sequences are next fed to a Bi-GRU, to produce the outputs $v$ (for the question) and $H_c$ (for the document) similar to the previous section.

SOLUTION:
How can a neural model be used for a retrieval if the input is the entire Wikipedia?