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 Q]: Following recent works, we compare on two widely used datasets, the Penn Treebank (PTB) BIBREF28 as prepared by BIBREF29 and WikiText2 (WT-2) BIBREF20 .
[EX A]: what data did they use?

[EX Q]: The BioNLP 2009 Shared Task BIBREF195 was based on the GENIA corpus BIBREF196 which contains PubMed abstracts of articles on transcription factors in human blood cells.
[EX A]: Which datasets are used in this work?

[EX Q]: The base sentence embedding model is a BiLSTM over all words in the respective sequences with randomly initialised word embeddings, following BIBREF30 . We opt for this strong baseline sentence encoding model, as opposed to engineering sentence embeddings that work particularly well for this dataset, to showcase the dataset. We would expect pre-trained contextual encoding models, e.g. ELMO BIBREF33 , ULMFit BIBREF34 , BERT BIBREF35 , to offer complementary performance gains, as has been shown for a few recent papers BIBREF36 , BIBREF37 .
[EX A]:
What were the baselines?