instruction:
In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
question:
We also are especially interested in seeing this model applied to different languages. 
 Question: Have the authors tried this approach on other languages?
answer:
Yes-no


question:
Throughout this article, we use two different embedding spaces. The first is the widely used representation built on GoogleNews BIBREF8 . The second is taken from BIBREF2 , and was trained on a Reddit dataset BIBREF9 . 
 Question: Which embeddings do they detect biases in?
answer:
Abstractive


question:
We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets.  
 Question: Are this techniques used in training multilingual models, on what languages?
answer:

Abstractive