Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ProphetNet-X: Large-Scale Pre-training Models for English, Chinese,
  Multi-lingual, Dialog, and Code Generation   Now, the pre-training technique is ubiquitous in natural language processing
field. ProphetNet is a pre-training based natural language generation method
which shows powerful performance on English text summarization and question
generation tasks. In this paper, we extend ProphetNet into other domains and
languages, and present the ProphetNet family pre-training models, named
ProphetNet-X, where X can be English, Chinese, Multi-lingual, and so on. We
pre-train a cross-lingual generation model ProphetNet-Multi, a Chinese
generation model ProphetNet-Zh, two open-domain dialog generation models
ProphetNet-Dialog-En and ProphetNet-Dialog-Zh. And also, we provide a PLG
(Programming Language Generation) model ProphetNet-Code to show the generation
performance besides NLG (Natural Language Generation) tasks. In our
experiments, ProphetNet-X models achieve new state-of-the-art performance on 10
benchmarks. All the models of ProphetNet-X share the same model structure,
which allows users to easily switch between different models. We make the code
and models publicly available, and we will keep updating more pre-training
models and finetuning scripts.

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The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
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The categories of this paper are: econ
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: hep-th
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg