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: The focus of this section is on recently published datasets and LID research applicable to the South African context. An in depth survey of algorithms, features, datasets, shared tasks and evaluation methods may be found in BIBREF0.

The datasets for the DSL 2015 & DSL 2017 shared tasks BIBREF1 are often used in LID benchmarks and also available on Kaggle . The recently published JW300 parallel corpus BIBREF2 covers over 300 languages with around 100 thousand parallel sentences per language pair on average. The WiLI-2018 benchmark dataset BIBREF4 for monolingual written natural language identification includes around 1000 paragraphs of 235 languages. The NCHLT text corpora BIBREF7 is likely a good starting point for a shared LID task dataset for the South African languages BIBREF8. 
 Question: Which datasets are employed for South African languages LID?

A: DSL 2015 DSL 2017 JW300 parallel corpus  NCHLT text corpora
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Q: For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question. 
 Question: How do they measure performance?

A: average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values
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Q: Intuitively, under-translated input words should contribute little to the NMT outputs, yielding much smaller word importance.  By exploiting the word importance calculated by Attribution method, we can identify the under-translation errors automatically without the involvement of human interpreters.  
 Question: How do they measure which words are under-translated by NMT models?

A:
They measured the under-translated words with low word importance score as calculated by Attribution.
method
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