Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Language Models with Transformers   The Transformer architecture is superior to RNN-based models in computational
efficiency. Recently, GPT and BERT demonstrate the efficacy of Transformer
models on various NLP tasks using pre-trained language models on large-scale
corpora. Surprisingly, these Transformer architectures are suboptimal for
language model itself. Neither self-attention nor the positional encoding in
the Transformer is able to efficiently incorporate the word-level sequential
context crucial to language modeling.
  In this paper, we explore effective Transformer architectures for language
model, including adding additional LSTM layers to better capture the sequential
context while still keeping the computation efficient. We propose Coordinate
Architecture Search (CAS) to find an effective architecture through iterative
refinement of the model. Experimental results on the PTB, WikiText-2, and
WikiText-103 show that CAS achieves perplexities between 20.42 and 34.11 on all
problems, i.e. on average an improvement of 12.0 perplexity units compared to
state-of-the-art LSTMs. The source code is publicly available.

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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
2
The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
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The categories of this paper are: supr-con