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: 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.

Output: How is the lexicon of trafficking flags expanded?

Input: Consider Input: We pair 11'248 standard German written words with their phonetical representations in six different Swiss dialects: Zurich, St. Gallen, Basel, Bern, Visp, and Stans (Figure FIGREF1).

Output: How many words are coded in the dictionary?

Input: Consider Input: Recently, Wen et al. wensclstm15 proposed an RNN-based approach, which outperformed previous methods on several metrics. However, the generated sentences often did not include all desired attributes.
Output: How is some information lost in the RNN-based generation models?