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.

[EX Q]: Maximum Entropy theory is applied to solve Vietnamese word segmentation BIBREF15 , BIBREF22 , BIBREF23 . There are several studies about the Vietnamese word segmentation task over the last decade. Dinh et al. started this task with Weighted Finite State Transducer (WFST) approach and Neural Network approach BIBREF9 . In addition, machine learning approaches are studied and widely applied to natural language processing and word segmentation as well. In fact, several studies used support vector machines (SVM) and conditional random fields (CRF) for the word segmentation task BIBREF7 , BIBREF8 . 
 Question: Which approaches have been applied to solve word segmentation in Vietnamese?
[EX A]: Maximum Entropy Weighted Finite State Transducer (WFST)  support vector machines (SVM) conditional random fields (CRF)

[EX 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?
[EX A]: English  German French

[EX Q]: We have collected more than 3,934,610 million tweets so far. 
 Question: How big is the dataset?
[EX A]:
more than 3,934,610 million tweets

