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 focus here on the Europarl domain, for which we have ample data in several languages, and use as in-domain training data the Europarl corpus BIBREF5 for two translation directions: English INLINEFORM0 German and English INLINEFORM1 French.  
 Question: what language is the data in?

A: English  German French
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Q: An acoustic word embedding is a function that takes as input a speech segment corresponding to a word, INLINEFORM0 , where each INLINEFORM1 is a vector of frame-level acoustic features, and outputs a fixed-dimensional vector representing the segment, INLINEFORM2 . 
 Question: How do they represent input features of their model to train embeddings?

A: a vector of frame-level acoustic features
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Q: A selection of results from this study are that:

Non-English code is a large-scale phenomena.

Transliteration is common in identifiers for all languages.

Languages clusters into three distinct groups based on how speakers use identifiers/comments/transliteration.

Non-latin script users write comments in their L1 script but write identifiers in English.

Right-to-left (RTL) language scripts, such as Arabic, have no observed prevalence on GitHub identifiers, implying that existing coders who speak RTL languages have substantial barriers in using their native script in code. 
 Question: What are results of public code repository study?

A:
Non-English code is a large-scale phenomena. Transliteration is common in identifiers for all languages. Languages clusters into three distinct groups based on how speakers use identifiers/comments/transliteration. Non-latin script users write comments in their L1 script but write identifiers in English. Right-to-left (RTL) language scripts, such as Arabic, have no observed prevalence on GitHub identifiers
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