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: 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?
Example Output: No attacks to any community  racist sexist homophobic religion based attacks attacks to other communities

Example Input: We evaluate our approach for six target languages: French (fr), Russian (ru), Arabic (ar), Chinese (zh), Hindi (hi), and Vietnamese (vi).  
 Question: What languages are the model transferred to?
Example Output: French (fr), Russian (ru), Arabic (ar), Chinese (zh), Hindi (hi), and Vietnamese (vi)

Example Input: The analysis of results from Tables TABREF17 , TABREF18 and TABREF19 show that 12 of 15 best results were obtained using new word embeddings. The evaluation results presented in Table TABREF20 (the chosen best embeddings models from Table TABREF19 ) prove that the best group of word embeddings is EC. The highest type F1-score was obtained for EC1 model, built using binary FastText Skip-gram method utilising subword information, with vector dimension equal to 300 and negative sampling equal to 10. The ability of the model to provide vector representation for the unknown words seems to be the most important. 
 Question: What conclusions are drawn from these experiments?
Example Output:
best results were obtained using new word embeddings best group of word embeddings is EC The highest type F1-score was obtained for EC1 model, built using binary FastText Skip-gram method utilising subword information ability of the model to provide vector representation for the unknown words seems to be the most important

