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.

Q: We use the Bayesian model of garg2012unsupervised as our base monolingual model. The semantic roles are predicate-specific. 
 Question: What does an individual model consist of?

A: Bayesian model of garg2012unsupervised as our base monolingual model
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Q:  For a fair comparison with previous work, we train the baseline MT system on the data released by BIBREF11.  
 Question: what was the baseline?

A:  MT system on the data released by BIBREF11
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Q: FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently. BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline. BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset. 
 Question: What is the baseline for the experiments?

A:
FastText BiLSTM BERT
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