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.

We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edgesthese may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges).
Did they use a relation extraction method to construct the edges in the graph?

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.
How is the lexicon of trafficking flags expanded?

For the Russian language, with its rich morphology, lemmatizing the training and testing data for ELMo representations yields small but consistent improvements in the WSD task.
What other examples of morphologically-rich languages do the authors give?