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.
--------
Question: In this work, we use pretraining on the unlabeled data of each task and show that it can increase the performance of classification systems. 
 Question: Where is MVCNN pertained?

Answer: on the unlabeled data of each task


Question: 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?

Answer: FastText BiLSTM BERT


Question: Because our approach is specifically intended to yield sentences that are grammatical, we additionally consider the following log ratio (i.e., the grammatical phrase over the ungrammatical phrase): 
 Question: How do they measure grammaticality?

Answer:
by calculating log ratio of grammatical phrase over ungrammatical phrase