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.

Example Input: Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets. Further, we labeled tweets as vulgar or hate speech.
Question: How many tweets are in the dataset?
Example Output: 10,000 Arabic tweet dataset

Example Input: We conduct experiments on our self-collected CAIS to evaluate the generalizability in different language. We apply two baseline models for comparison, one is the popular BiLSTMs + CRF architecture BIBREF36 for sequence labeling task, and the other one is the more powerful sententce-state LSTM BIBREF21. The results listed in Table TABREF50 demonstrate the generalizability and effectiveness of our CM-Net when handling various domains and different languages.
Question: What were the baselines models?
Example Output: BiLSTMs + CRF architecture BIBREF36 sententce-state LSTM BIBREF21

Example Input: We employ precision, recall, F1 and accuracy for evaluation metrics.
Question: what evaluation metrics are reported?
Example Output:
precision, recall, F1 and accuracy