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: In this work, we develop a technique to rapidly transfer an existing pre-trained model from English to other languages in an energy efficient way BIBREF8.  
 Question: How much training data from the non-English language is used by the system?

Output: No data. Pretrained model is used.


Input: Consider Input: We choose Zulu (ZUL), a Bantoid language. In contrast to the first two, it is strongly prefixing. 
 Question: What is an example of a prefixing language?

Output: Zulu


Input: Consider Input: Another problem we face is that the BERT model released by Google is trained only on Arabic Wikipedia, which is almost exclusively Modern Standard Arabic (MSA). The shared task dataset contains 5,030 tweets related to different political issues and events in the Middle East taking place between 2011 and 2018. Tweets involve both MSA as well as dialects at various degrees of granularity such as Egyptian, Gulf, and Levantine. 
 Question: What dialect is used in the Google BERT model and what is used in the task data?


Output: Modern Standard Arabic (MSA) MSA as well as dialects at various degrees of granularity such as Egyptian, Gulf, and Levantine