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]: Despite this, we found that the training of our embeddings was not considerably slower than the training of order-2 equivalents such as SGNS. Explicitly, our GPU trained CBOW vectors (using the experimental settings found below) in 3568 seconds, whereas training CP-S and JCP-S took 6786 and 8686 seconds respectively. 
 Question: Do they measure computation time of their factorizations compared to other word embeddings?
[EX A]: Yes

[EX Q]: We introduce our proposed diversity, density, and homogeneity metrics with their detailed formulations and key intuitions. 
 Question: Did they propose other metrics?
[EX A]: Yes

[EX Q]: Multiple papers have shown that 'shallow' naive Bayes classifiers BIBREF12, BIBREF8, BIBREF13, BIBREF14, SVMs BIBREF15 and similar models work very well for doing LID. The DSL 2017 paper BIBREF1, for example, gives an overview of the solutions of all of the teams that competed on the shared task and the winning approach BIBREF16 used an SVM with character n-gram, parts of speech tag features and some other engineered features. The winning approach for DSL 2015 used an ensemble naive Bayes classifier. The fasttext classifier BIBREF17 is perhaps one of the best known efficient 'shallow' text classifiers that have been used for LID . Multiple papers have proposed hierarchical stacked classifiers (including lexicons) that would for example first classify a piece of text by language group and then by exact language BIBREF18, BIBREF19, BIBREF8, BIBREF0. Researchers have investigated deeper LID models li 
 Question: What is the approach of previous work?
[EX A]:
'shallow' naive Bayes SVM hierarchical stacked classifiers bidirectional recurrent neural networks