Question: What is the approach of previous work?

Evidence:
raw_evidence: Existing NLP datasets, models and services BIBREF10 are available for South African languages. These include an LID algorithm BIBREF11 that uses a character level n-gram language model. 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. Some work has also been done on classifying surnames between Tshivenda, Xitsonga and Sepedi BIBREF20. Additionally, data augmentation BIBREF21 and adversarial training BIBREF22 approaches are potentially very useful to reduce the requirement for data.
Researchers have investigated deeper LID models like bidirectional recurrent neural networks BIBREF23 or ensembles of recurrent neural networks BIBREF24. The latter is reported to achieve 95.12% in the DSL 2015 shared task. In these models text features can include character and word n-grams as well as informative character and word-level features learnt BIBREF25 from the training data. The neural methods seem to work well in tasks where more training data is available.
raw_evidence: Existing NLP datasets, models and services BIBREF10 are available for South African languages. These include an LID algorithm BIBREF11 that uses a character level n-gram language model. 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. Some work has also been done on classifying surnames between Tshivenda, Xitsonga and Sepedi BIBREF20. Additionally, data augmentation BIBREF21 and adversarial training BIBREF22 approaches are potentially very useful to reduce the requirement for data.
Researchers have investigated deeper LID models like bidirectional recurrent neural networks BIBREF23 or ensembles of recurrent neural networks BIBREF24. The latter is reported to achieve 95.12% in the DSL 2015 shared task. In these models text features can include character and word n-grams as well as informative character and word-level features learnt BIBREF25 from the training data. The neural methods seem to work well in tasks where more training data is available.

Answers:
'shallow' naive Bayes, SVM, hierarchical stacked classifiers, bidirectional recurrent neural networks
BIBREF11 that uses a character level n-gram language model, 'shallow' naive Bayes classifiers BIBREF12, BIBREF8, BIBREF13, BIBREF14, SVMs BIBREF15, 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, hierarchical stacked classifiers (including lexicons), bidirectional recurrent neural networks BIBREF23 or ensembles of recurrent neural networks BIBREF24