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: If we re-train the skip-gram model and update the emoji map periodically on new escort ads, when traffickers switch to new emojis, the map can link the new emojis to the old ones, assisting anti-trafficking experts in expanding the lexicon of trafficking flags. This approach also works for acronyms and deliberate typos.
Example Output: How is the lexicon of trafficking flags expanded?

Example Input: We find that while optimizing its reformulations to adapt to the language of the QA system, AQA diverges from well structured language in favour of less fluent, but more effective, classic information retrieval (IR) query operations. 
Example Output: What is the difference in findings of Buck et al? It looks like the same conclusion was mentioned in Buck et al..

Example Input: In tab:blueperpl we report BLEU scores for the reconstruction of test set sentences from their content and feature representations, as well as the model perplexities of the reconstruction. For both models, we use beam decoding with a beam size of eight. Note that for both models, the all and top classification accuracy tends to be quite similar, though for the Baseline they are often almost exactly the same when the Baseline has little to no diversity in the outputs.
Example Output:
What metrics are used for automatic evaluation?