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The ability to generalize is a real differentiator between grammar error correction systems, including Writer and Grammarly’s.

And at Writer, we are very careful about the training data we feed our model, because good data helps the model generalize well. It doesn't just memorize examples, but gets a 'feel' for things.

We do this millions and millions of times, and the model learns. Then, we would change the verb 'write' to the incorrect 'writes': Writer's AI writing assistant helps everyone at your company writes with the same style, terminology, and brand voice and tell the model that this is a bad sentence. We might show it Writer's AI writing assistant helps everyone at your company write with the same style, terminology, and brand voice and tell the model that this is a good sentence. For example, we teach the Writer deep learning grammar error correction system to correct subject/verb agreement by showing it many examples of subjects and verbs. Deep learning systems start out with the capacity to learn, and then are trained to recognize patterns by being shown many examples. That's why Writer uses techniques from artificial intelligence, such as deep learning.There are too many ways for the English language to work, so a mostly valid rule could break in some contexts. Even with an army of linguists, you just couldn’t cover all the possible English grammatical errors. There are relatively few always-true, easy rules that can be programmed. However, the vast majority of errors we would want to catch are contextual. This works well when the problem isn't contextual, ie, a rule that says, anytime someone types teh they actually meant the.

Most grammar checkers use rules to identify English writing errors and suggest corrections.

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