Today, there are master students of my lab that are preparing for their thesis presentation. One biggest demands of them is to check false Chinese characters in their thesis. For example:
This exmaple is borrowed from FASPell from
iqiyi, a Chinese IT company.
But when I test this example on the following online demo of Chinese Spell Correctors, they all failed.
('国际电台苦名坚持人', [('丰', '坚', 6, 7)])只 in 一只街, but it seems unable to correct wrong characters such as 丰持人, which is a wierd phrase国际电台著名丰持人 can be changed to 国际电台著名主持人, however, the input 中国北境市举办运动会 cannot be corrected to 北京市, however pycorrector can (('中国北京市举办运动会', [('境', '京', 3, 4)]))So, the performance of current Chinese spelling checking algorithms/models is very unsatisfactory. But what are the challenges?
The above test examples mainly focus on wrong Chinese character(s) in a Chinese phrase within a context. The following reference gives modelling methods and benchmarks for Chinese spelling correction.
One reason for my feeling of the bad performance of the online demo might be my expectation of real products. It is interesting to discuss the difference and connection between academic papers and real productions. When I find that pycorrector can achieve very high performance on benchmarks, I relieve a lot. However, when I first treat it as product, I think its over-confidence to change 丰持人 to 坚持人 is unbearable.
So it is better to think of the interaction and presentation of model’s prediction with/to end users. So what is a better way to fix this bias?
One solution might be first showing the model’s confidence and results of detecting errors; and then the potential correction choices that might contain the user expected outcome.
Maybe conformal prediction can used here as well.
tensorflow.