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  "author": "Yi Yang",
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  "author": "Yi Yang",
            
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  "citation": [
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  "citation": [
            
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    "https://doi.org/10.48550/arXiv.2305.17197",
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    "https://doi.org/10.48550/arXiv.2305.17197",
            
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    "https://doi.org/10.48550/arXiv.2211.09744",
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  "doi": "10.57702/byuoeozp",
            
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Chen Zhang",
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      "extra_author": "Chen Zhang",
            
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      "orcid": ""
            
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    },
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    {
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      "extra_author": "Dawei Song",
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      "extra_author": "Dawei Song",
            
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  "name": "glue",
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  "name": "glue",
            
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  "notes": "Pre-trained language models (PrLM) have to carefully 
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  "notes": "Pre-trained language models (PrLM) have to carefully 
            
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manage input units when training on a very large text with a 
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manage input units when training on a very large text with a 
            
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vocabulary consisting of millions of words. Previous works have shown 
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vocabulary consisting of millions of words. Previous works have shown 
            
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that incorporating span-level information over consecutive words in 
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that incorporating span-level information over consecutive words in 
            
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pre-training could further improve the performance of PrLMs.",
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pre-training could further improve the performance of PrLMs.",
            
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      "display_name": "natural language understanding",
            
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      "name": "natural language understanding",
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      "name": "natural language understanding",
            
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      "id": "ab481ec4-1a05-4f68-96d7-44beb1d10f63",
            
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      "name": "sentiment analysis",
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    {
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      "display_name": "text classification",
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      "display_name": "text classification",
            
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      "id": "98d3b2df-ea96-4826-971b-6c5bf64c999d",
            
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  ],
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  ],
            
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  "title": "GLUE",
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  "title": "GLUE",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}