In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

We consider two different models for each language pair: the Baseline and the Document model. We evaluate them on 3 test sets and report BLEU and TER scores. All experiments are run 8 times with different seeds, we report averaged results and p-values for each experiment.
What evaluation metrics did they use?

In this approach the similarity between two words is not strictly based on their cooccurrence frequencies, but rather on the frequencies of the other words which occur with both of them (i.e., second order cooccurrences). This approach has been shown to be successful in quantifying semantic relatedness BIBREF12 , BIBREF13 .
What is a second order co-ocurrence matrix?

In this section, we describe the data augmentation methods we use to increase the amount of training data in order to make our NMT systems suffer less from the low-resourced situation in Japanese INLINEFORM0 Vietnamese translation. Back Translation
One of the approaches to leverage the monolingual data is to use a machine translation system to translate those data in order to create a synthetic parallel data. Normally, the monolingual data in the target language is translated, thus the name of the method: Back Translation BIBREF11 . Mix-Source Approach
Another data augmentation method considered useful in this low-resourced setting is the mix-source method BIBREF12 .
what methods were used to reduce data sparsity effects?