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]: For automatically evaluating our methods, we propose to use widely used metric for image/video captioning. This is because the proposed CommonGen task can be regarded as also a caption task where the context are incomplete scenes with given concept-sets. Therefore, we choose BLEU-3/4 BIBREF28, ROUGE-2/L BIBREF29, CIDEr BIBREF30, and SPICE BIBREF31 as the main metrics. Apart from these classic metrics, we also include a novel embedding-based metric named BERTScore BIBREF32. To make the comparisons more clear, we show the delta of BERTScore results by subtracting the score of merely using input concept-sets as target sentences, named $ riangle $BERTS.
Question: What automatic metrics are used for this task?
[A]: BLEU-3/4 ROUGE-2/L CIDEr SPICE BERTScore

[Q]: The reviews we used have been extracted from TripAdvisor and originally proposed in BIBREF10 , BIBREF11 .
Question: Where are the hotel reviews from?
[A]: TripAdvisor

[Q]: 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 .
Question: what methods were used to reduce data sparsity effects?
[A]:
Back Translation Mix-Source Approach