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 Q]: Furthermore, the basic seq2seq assumes a strict order between generated tokens, but in reality, we should not severely punish the model when it predicts the correct tokens in the wrong order. 
 Question: Do they impose any grammatical constraints over the generated output?
[EX A]: No

[EX Q]: We use Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model.  
 Question: What baseline is used?
[EX A]: Waseem and Hovy BIBREF5 Davidson et al. BIBREF9 Waseem et al. BIBREF10

[EX Q]: We use three creative English datasets with distinct linguistic characteristics: (1) A corpus of 740 classical and contemporary English poems, (2) a corpus of 14950 metaphor sentences retrieved from a metaphor database website and (3) a corpus of 1500 song lyrics ranging across genres.  We first pre-train our generator on the Gutenberg dataset BIBREF24 for 20 epochs and then fine-tune BIBREF19 them to our target datasets with a language modeling objective. 
 Question: Which datasets are used?
[EX A]:
A corpus of 740 classical and contemporary English poems  a corpus of 14950 metaphor sentences retrieved from a metaphor database website  a corpus of 1500 song lyrics ranging across genres Gutenberg dataset