In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

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.
Question: How is the lexicon of trafficking flags expanded?

Output: 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

Input: Consider Input: As a framework, we use the sentence classifier configuration of FLAIR BIBREF46 with a biLSTM encoder/classifier architecture fed by character and word level representations composed of a concatenation of fixed 300 dimensional GloVe embeddings BIBREF47, pre-trained contextualized FLAIR word embeddings, and pre-trained contextualized character embeddings from AllenNLP BIBREF48 with FLAIR's default hyperparameters.
Question: Are some models evaluated using this metric, what are the findings?

Output: Yes

Input: Consider Input: For instance, schema 23 from the WSC collection can be translated into both "The girls were bullying the boys so we [punished/rescued] them" and "The boys were bullying the girls, so we [punished/rescued] them," thus avoiding any presupposition of whether girls are more likely to bully boys or vice versa.
Question: What data do they look at?
Output: WSC collection