  This paper studies the efficiency of transferring BERT learnings to low
complexity models like BiLSTM, BiLSTM with attention and shallow CNNs using
sentiment analysis on SST-2 dataset. It also compares the complexity of
inference of the BERT model with these lower complexity models and underlines
the importance of these techniques in enabling high performance NLP models on
edge devices like mobiles, tablets and MCU development boards like Raspberry Pi
etc. and enabling exciting new applications.
