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.

Example Input: For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases.
Example Output: How many paraphrases are generated per question?

Example Input: For this knowledge, we create a knowledge base by counting how many times a predicate-argument tuple appears in a corpus and use the resulted number to represent the preference strength. Specifically, we use the English Wikipedia as the base corpus for such counting.
Example Output: What is the source of external knowledge?

Example Input: Long-short Term Hybrid Memory and Multi-attention Block. Long-short Term Hybrid Memory (LSTHM) is an extension of the Long-short Term Memory (LSTM) by reformulating the memory component to carry hybrid information. In the LSTHM model, we seek to build a memory mechanism for each modality which in addition to storing view-specific dynamics, is also able to store the cross-view dynamics that are important for that modality. This allows the memory to function in a hybrid manner.
Example Output:
What is the difference between Long-short Term Hybrid Memory and LSTMs?