Distilling Neural Networks for Faster and Greener Dependency ParsingDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Withdrawn SubmissionReaders: Everyone
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  • TL;DR: We increase the efficiency of neural network dependency parsers with teacher-student distillation.
  • Abstract: The carbon footprint of natural language processing (NLP) research has been increasing in recent years due to its reliance on large and inefficient neural network implementations. Distillation is a network compression technique which attempts to impart knowledge from a large model to a smaller one. We use teacher-student distillation to improve the efficiency of the Biaffine dependency parser which obtains state-of-the-art performance with respect to accuracy and parsing speed (Dozat & Manning, 2016). When distilling to 20% of the original model’s trainable parameters, we only observe an average decrease of ∼1 point for both UAS and LAS across a number of diverse Universal Dependency treebanks while being 2.26x (1.21x) faster than the baseline model on CPU (GPU) at inference time. We also observe a small increase in performance when compressing to 80% for some treebanks. Finally, through distillation we attain a parser which is not only faster but also more accurate than the fastest modern parser on the Penn Treebank.
  • Keywords: dependency parsing, efficiency, green AI, compression, distillation, syntax, NLP
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