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.

Example Input: We conduct experiments on our self-collected CAIS to evaluate the generalizability in different language. We apply two baseline models for comparison, one is the popular BiLSTMs + CRF architecture BIBREF36 for sequence labeling task, and the other one is the more powerful sententce-state LSTM BIBREF21. The results listed in Table TABREF50 demonstrate the generalizability and effectiveness of our CM-Net when handling various domains and different languages. 
 Question: What were the baselines models?
Example Output: BiLSTMs + CRF architecture BIBREF36 sententce-state LSTM BIBREF21

Example Input: In this paper, we introduce automatic `drunk-texting prediction' as a computational task. Given a tweet, the goal is to automatically identify if it was written by a drunk user.  
 Question: Do the authors equate drunk tweeting with drunk texting? 
Example Output: Yes

Example Input: We first run our experiment on BiLSTM, BiLSTM-CNN, BiLSTM-CRF BiLSTM-CNN-CRF using the hyper-parameters mentioned in Table TABREF30.  
 Question: Which machine learning models do they explore?
Example Output:
BiLSTM BiLSTM-CNN BiLSTM-CRF BiLSTM-CNN-CRF

