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: Table TABREF20 shows that, on WoZ and DSTC2 datasets, SIM model has the same number of parameters, which is only 23% and 19% of that in GLAD model. 
 Question: How do they measure model size?
Example Output: By the number of parameters.

Example Input: We compare our sentence classification with the LSTM baseline and evaluate the biggest set of PICO sentence data available at this point BIBREF13. SCIBERT was chosen as an additional baseline model for fine-tuning because it provided the best representation of embedded PICO sentences.  
 Question: What baselines did they consider?
Example Output: LSTM SCIBERT

Example Input: We assume that speech transcripts can be extracted from audio signals with high accuracy, given the advancement of ASR technologies BIBREF7 . 
 Question: Do they use datasets with transcribed text or do they determine text from the audio?
Example Output:
They use text transcription.