One of the projects I&#x27;d love to develop is an automated peer editor for student essays.  My wife is an english teacher and a large percentage of her time is taken up by grading papers.  A large percentage of that time is then spent marking up grammar and spelling.  What I envision is a website that handles that grammar&#x2F;spelling bit.  More importantly, I&#x27;d like it as a tool that the students use freely prior to submitting their essays to the teacher.  I want them to have immediate feedback on how to improve the grammar in their essays, so they can iterate and learn.  By the time the essays reach the teacher, the teacher should only have to grade for content, composition, style, plagiarism, citations, etc.  Hopefully this also helps to reduce the amount of grammar that needs to be taught in-class, freeing time for more meaningful discussions.<p>The problem is that while I have knowledge and experience in the computer vision side of machine learning, I lack experience in NLP.  And to the best of my knowledge NLP as a field has not come as far as vision, to the extent that such an automated editor would have too many mistakes.  To be student facing it would need to be really accurate.  On top of that it wouldn&#x27;t be dealing with well formed input.  The input by definition is adversarial.  So unlike SyntaxNet which is built to deal with comprehensible sentences, this tool would need to deal with incomprehensible sentences.  According to the link, SyntaxNet only gets 90% accuracy on random sentences from the web.<p>That said, I might give SyntaxNet a try.  The idea would be to use SyntaxNet to extract meaning from a broken sentence, and then work backwards from the meaning to identify how the sentence can be modified to better match that meaning.<p>Thank you Google for contributing this tool to the community at large.