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arXiv · 2104.07951

Optimal Size-Performance Tradeoffs: Weighing PoS Tagger Models

Magnus Jacobsen·Mikkel H. Sørensen·Leon Derczynski
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Abstract

Improvement in machine learning-based NLP performance are often presented with bigger models and more complex code. This presents a trade-off: better scores come at the cost of larger tools; bigger models tend to require more during training and inference time. We present multiple methods for measuring the size of a model, and for comparing this with the model's performance. In a case study over part-of-speech tagging, we then apply these techniques to taggers for eight languages and present a novel analysis identifying which taggers are size-performance optimal. Results indicate that some classical taggers place on the size-performance skyline across languages. Further, although the deep models have highest performance for multiple scores, it is often not the most complex of these that reach peak performance.

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Magnus Jacobsen, Mikkel H. Sørensen, Leon Derczynski. 2021-04-16. Optimal Size-Performance Tradeoffs: Weighing PoS Tagger Models. https://arxiv.org/abs/2104.07951

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