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]: To test the difficulty of our dataset, we checked the majority class label and the accuracies of five state-of-the-art NLI models adopting different approaches: BiMPM (Bilateral Multi-Perspective Matching Model; BIBREF31 , BIBREF31 ), ESIM (Enhanced Sequential Inference Model; BIBREF32 , BIBREF32 ), Decomposable Attention Model BIBREF33 , KIM (Knowledge-based Inference Model; BIBREF34 , BIBREF34 ), and BERT (Bidirectional Encoder Representations from Transformers model; BIBREF35 , BIBREF35 ). Regarding BERT, we checked the performance of a model pretrained on Wikipedia and BookCorpus for language modeling and trained with SNLI and MultiNLI. 
 Question: What NLI models do they analyze?
[EX A]: BiMPM ESIM Decomposable Attention Model KIM BERT

[EX Q]: This section introduces our probabilistic model that infers keyword expectation and trains the target model simultaneously. 
 Question: What type of classifiers are used?
[EX A]: probabilistic model

[EX Q]: the facts that are relevant for answering a particular question) are labeled during training. 
 Question: What does supporting fact supervision mean?
[EX A]:
 the facts that are relevant for answering a particular question) are labeled during training.