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.

[Q]: 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?
[A]: No attacks to any community  racist sexist homophobic religion based attacks attacks to other communities


[Q]: We recruited 100 crowdworkers on Amazon Mechanical Turk (AMT) and measured completion times and accuracies for typing randomly sampled sentences from the Yelp corpus.  
 Question: What user variations have been tested?
[A]: completion times and accuracies 


[Q]: We discuss two core models for addressing sequence labeling problems and describe, for each, training them in a single-model multilingual setting: (1) the Meta-LSTM BIBREF0 , an extremely strong baseline for our tasks, and (2) a multilingual BERT-based model BIBREF1 . 
 Question: What is the multilingual baseline?
[A]:
 the Meta-LSTM BIBREF0