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.

Ex Input:
Even if LangID-High does not present a more accurate result, it does present a more compact one: LangID-High is 15.4 MB, while the combined wFST high resource models are 197.5 MB.

Ex Output:
how is model compactness measured?


Ex Input:
We compare our personalized models against two baselines. The first is a name-based Nearest-Neighbor model (NN). We initially adapted the Neural Checklist Model of BIBREF0 as a baseline; however, we ultimately use a simple Encoder-Decoder baseline with ingredient attention (Enc-Dec), which provides comparable performance and lower complexity.

Ex Output:
What are the baseline models?


Ex Input:
Datasets
We conduct experiments on two Chinese question answering datasets from NLPCC-2016 evaluation task BIBREF13 . DBQA is a document based question answering dataset.  KBRE is a knowledge based relation extraction dataset.

Ex Output:

Which dataset(s) do they evaluate on?