We construct a dataset through entries with multiple meanings and examples from Chinese dictionaries, and set the example as context and explanations as choices, the goal of Polysemy Machine Comprehension (PolyMRC) is to find the correct explanation of the entry in the example.

the statistics of the dataset

| | | |
|--|--|--|
|split|sentences|average sentence length|
|train|46,119|38.55|
|validation|5,765|38.31|
|test|5,765|38.84|
task_categories:multiple-choice size_categories:10M<n<100M language:zh license:apache-2.0 region:us
{"language": ["zh"], "license": "apache-2.0", "size_categories": ["10M<n<100M"], "task_categories": ["multiple-choice"]}
TAGS
#task_categories-multiple-choice #size_categories-10M<n<100M #language-Chinese #license-apache-2.0 #region-us 

We construct a dataset through entries with multiple meanings and examples from Chinese dictionaries, and set the example as context and explanations as choices, the goal of Polysemy Machine Comprehension (PolyMRC) is to find the correct explanation of the entry in the example.


the statistics of the dataset







TAGS
#task_categories-multiple-choice #size_categories-10M<n<100M #language-Chinese #license-apache-2.0 #region-us 

passage: TAGS
#task_categories-multiple-choice #size_categories-10M<n<100M #language-Chinese #license-apache-2.0 #region-us 
