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.

Ex Input:
We compare our sentence classification with the LSTM baseline and evaluate the biggest set of PICO sentence data available at this point BIBREF13. SCIBERT was chosen as an additional baseline model for fine-tuning because it provided the best representation of embedded PICO sentences.
Question: What baselines did they consider?

Ex Output:
LSTM SCIBERT

Ex Input:
Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts.
Question: What data is the language model pretrained on?

Ex Output:
Chinese general corpus

Ex Input:
INLINEFORM0 in the subsampled BIBREF2 training corpus and incrementing cell INLINEFORM1 for every context word INLINEFORM2 appearing within this window (forming a INLINEFORM3 pair). LexVec adjusts the PPMI matrix using context distribution smoothing BIBREF3 . We compare 1) the use of n-gram subwords, like fastText, and 2) unsupervised morphemes identified using Morfessor BIBREF11 to learn whether more linguistically motivated subwords offer any advantage over simple n-grams.
Question: What types of subwords do they incorporate in their model?

Ex Output:
n-gram subwords unsupervised morphemes identified using Morfessor BIBREF11 to learn whether more linguistically motivated subwords