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:
In this section, we introduce an invertibility condition for our neural projector to tackle the optimization challenge. Specifically, we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists.  
 Question: What is the invertibility condition?

Ex Output:
The neural projector must be invertible.


Ex Input:
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?

Ex Output:
FastText BiLSTM BERT


Ex Input:
 We modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions. 
 Question: What are all the metrics to measure user engagement?

Ex Output:

overall rating mean number of turns


