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 develop a null model to determine how much variation in binomial orderings we might expect across communities and across time, if binomial orderings were randomly ordered according to global asymmetry values.  
 Question: What new model is proposed for binomial lists?

Ex Output:
null model 


Ex Input:
In order to verify the effectiveness of our method (i.e., Multi-linear attention) replacing multi-head attention in Transformer, we carry out two NLP tasks named language modeling (LM) and neural machine translation (NMT). Then, we test different model configurations on the PTB BIBREF25 , WikiText-103 BIBREF26 and One-Billion Word benchmark BIBREF27 datasets and report the results in Table 1 and Table 2 . In this task, we have trained the Transformer model BIBREF2 on WMT 2016 English-German dataset BIBREF36 . 
 Question: What datasets or tasks do they conduct experiments on?

Ex Output:
Language Modeling (LM) PTB BIBREF25 , WikiText-103 BIBREF26 and One-Billion Word benchmark BIBREF27 datasets neural machine translation (NMT) WMT 2016 English-German dataset


Ex Input:
We evaluate with all commonly-used metrics in question generation BIBREF13: BLEU-1 (B1), BLEU-2 (B2), BLEU-3 (B3), BLEU-4 (B4) BIBREF17, METEOR (MET) BIBREF18 and ROUGE-L (R-L) BIBREF19. We use the evaluation script released by Chen2015MicrosoftCC. 
 Question: What metrics do they use?

Ex Output:

BLEU-1 (B1), BLEU-2 (B2), BLEU-3 (B3), BLEU-4 (B4) BIBREF17, METEOR (MET) BIBREF18 and ROUGE-L (R-L) BIBREF19