3 ms·
It would be interesting to see how something like this could be integrated into Quickcheck. However, it is not clear to me how you would use it to generate erro
by anonymousDan 16y ago
It would be interesting to see how something like this could be integrated into Quickcheck. However, it is not clear to me how you would use it to generate erroneous input. In the example he gives the training relies on a sample of correct makefiles. Would it not be useful to also have a separate network that produce makefiles that should probably cause an error?
- ezy 16y agoIt really depends on what units of data your chain (in this case, an n-gram model) is dealing with. You can increase or decrease the granularity and modeling depth to get different kind of erroneous data using the same training data. For example, given the same corpus, if you have an n-gram model of english characters, obviously some[1] of the words will be erroneous. But if that model is over words, none of the words will be erroneous, but the grammar will still be broken in some cases. Extend that to an n-gram over collections of grammatical phrases where the words, grammar might be correct, but it may have a nonsensical meaning in some cases, etc. [1] Obviously "some" is dependent on the amount and diversity of the training data, model detail, etc.