TASK DEFINITION: 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.
PROBLEM: We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities.
Question: What annotations are available in the dataset - tweat used hate speach or not?

SOLUTION: No attacks to any community racist sexist homophobic religion based attacks attacks to other communities

PROBLEM: 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?

SOLUTION: 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

PROBLEM: Following lebret2016neural, we used BLEU-4, NIST-4 and ROUGE-4 as the evaluation metrics.
Question: What metrics are used for evaluation?

SOLUTION:
BLEU-4 NIST-4 ROUGE-4