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 crowdsourced multiple-choice questions in two parts, encouraging workers to be imaginative and varied in their use of language. First, workers were given a seed qualitative relation q+/-( INLINEFORM0 ) in the domain, expressed in English (e.g., “If a surface has more friction, then an object will travel slower”), and asked to enter two objects, people, or situations to compare. 
 Question: Do all questions in the dataset allow the answers to pick from 2 options?

A: Yes
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Q:  To be able to employ the trained model, test sets are first translated to English via machine translation and then inference takes place.   For machine translation, Google translation API is used. 
 Question: how did the authors translate the reviews to other languages?

A: Using Google translation API.
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Q: In this work, we analyze a set of tweets related to a specific classical music radio channel, BBC Radio 3, interested in detecting two types of musical named entities, Contributor and Musical Work. 
 Question: What language is the Twitter content in?

A:
English
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