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:  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: All cases exhibit high scores—in the vast majority of the cases substantially higher than reported in previous work. In particular, in BIBREF1 we assess the ability of LSTMs to learn subject-verb agreement patterns in English, and evaluate on naturally occurring wikipedia sentences.  BIBREF2 also consider subject-verb agreement, but in a “colorless green ideas” setting in which content words in naturally occurring sentences are replaced with random words with the same part-of-speech and inflection, thus ensuring a focus on syntax rather than on selectional-preferences based cues.  BIBREF3 consider a wider range of syntactic phenomena (subject-verb agreement, reflexive anaphora, negative polarity items) using manually constructed stimuli, allowing for greater coverage and control than in the naturally occurring setting. 
 Question: Were any of these tasks evaluated in any previous work?

A: Yes
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Q: In this work, we use the datasets released by BIBREF1 and HEOT dataset provided by BIBREF0 .  This was then followed by transliteration using the Xlit-Crowd conversion dictionary and translation of each word to English using Hindi to English dictionary.  
 Question: Do all the instances contain code-switching?

A:
No
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