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.
--------
Question: 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?

Answer: 10,000 Arabic tweet dataset 


Question: We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English.  
 Question: Which languages are used in the multi-lingual caption model?

Answer: German-English, French-English, and Japanese-English


Question: Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin.  Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. 
 Question: What are the specific tasks being unified?

Answer:
 three types of questions, namely tumor size, proximal resection margin and distal resection margin