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]: We use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German. 
 Question: what language pairs are explored?
[A]: German-English Turkish-English English-German


[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 $\triangle $BERTS. 
 Question: What automatic metrics are used for this task?
[A]: BLEU-3/4 ROUGE-2/L CIDEr SPICE BERTScore


[Q]: In CBOW architecture the task is predicting the word given its context and in SG the task in predicting the context given the word. We built 16 models of word embeddings using the implementation of CBOW and Skip-gram methods in the FastText tool BIBREF9 . 
 Question: What is specific about the specific embeddings?
[A]:
predicting the word given its context