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Progress and Tradeoffs in Neural Language Models
[article]
2018
arXiv
pre-print
In recent years, we have witnessed a dramatic shift towards techniques driven by neural networks for a variety of NLP tasks. Undoubtedly, neural language models (NLMs) have reduced perplexity by impressive amounts. This progress, however, comes at a substantial cost in performance, in terms of inference latency and energy consumption, which is particularly of concern in deployments on mobile devices. This paper, which examines the quality-performance tradeoff of various language modeling
arXiv:1811.00942v1
fatcat:h2e3nyv2y5eu7e4oniuepy65ty