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:
mcdonald:11 established that, when no treebank annotations are available in the target language, training on multiple source languages outperforms training on one (i.e., multi-source model transfer outperforms single-source model transfer). Following guo:16, for each target language, we train the parser on six other languages in the Google universal dependency treebanks version 2.0 (de, en, es, fr, it, pt, sv, excluding whichever is the target language), and we use gold coarse POS tags.

Ex Output:
How does the model work if no treebank is available?


Ex Input:
To build the large ancient-modern Chinese dataset, we collected 1.7K bilingual ancient-modern Chinese articles from the internet. More specifically, a large part of the ancient Chinese data we used come from ancient Chinese history records in several dynasties (about 1000BC-200BC) and articles written by celebrities of that era.

Ex Output:
Where does the ancient Chinese dataset come from?


Ex Input:
However, the coherence of the topics produced by LDA is poorer than expected.

To address this lack of coherence, we applied non-negative matrix factorization (NMF).

Ex Output:

How are prominent topics idenified in Dabiq and Rumiyah?