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


[Q]: In its general form, a type-logical grammar consists of following components: 
 Question: Does Grail accept Prolog inputs?
[A]: No


[Q]:  The flexibility of the DNN model allowed us to include many more surface level features such as affixes, leading and trailing characters in words and stems, and the presence of words in large gazetteers of named entities. As we show later, these additional features significantly lowered CEER. 
 Question: what surface-level features are used?
[A]:
affixes, leading and trailing characters in words and stems, and the presence of words in large gazetteers of named entities