Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Survey on Model Compression for Natural Language Processing   With recent developments in new architectures like Transformer and
pretraining techniques, significant progress has been made in applications of
natural language processing (NLP). However, the high energy cost and long
inference delay of Transformer is preventing NLP from entering broader
scenarios including edge and mobile computing. Efficient NLP research aims to
comprehensively consider computation, time and carbon emission for the entire
life-cycle of NLP, including data preparation, model training and inference. In
this survey, we focus on the inference stage and review the current state of
model compression for NLP, including the benchmarks, metrics and methodology.
We outline the current obstacles and future research directions.

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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ex
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The categories of this paper are: econ
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The categories of this paper are: hep-th
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The categories of this paper are: nlin