|
|
|
|
There's an old saying in robotics: Anything a human being learns to do after age 5 is easy to teach a machine. Everything we learn before 5, not so easy. That unwritten law of machine learning might explain why there are computers that can beat the world's best chess and Go masters, but we've yet to build a robot that can walk like a human. This might also explain why the spellchecker on your computer works so brilliantly, but the grammar checker doesn't. We learn how to spell only when we're old enough to go to school, but the basics of language development can start as early as in the womb. English grammar, on the other hand, contains a near infinite number of possibilities, and whether something is grammatically correct or incorrect can largely depend on subtle clues like context and inference. That's why certain English sentences are such a pain in the neck for automated grammar checkers. So much of English grammar involves inference and something called mutual contextual beliefs," says Perelman. "When I make a statement, I believe that you know what I know about this.
Machines aren't that smart. Mar Ginés Marín is a principal program manager at Microsoft who's been tinkering with the Office grammar editor for the past 17 years. She says that in the early days, the best Word could do was parse a sentence into its component parts of speech and identify simple grammar errors like noun-verb agreement. Then engineers figured out how to parse a sentence into smaller "chunks" of two or three words to target things like "a/an" agreement. This is called natural language processing or NLP. Susan Hendrich is a group program manager at Microsoft in charge of the natural language processing teams working on Office. With machine learning, Microsoft engineers could go beyond programming each and every grammar rule into the software. Instead, they train the machine on a huge dataset of correct English usage and let the machine learn from the patterns it discovers. Hendrich says that algorithms developed by Microsoft through machine learning are what drive Word's decisions about whether or not a sentence needs a question mark, or what types of clauses require a comma (pretty tricky stuff, even for us human writers).
But did it work? Daniel Kies, an English professor at the College of Du Page, in Glen Ellyn, Illinois, once conducted a head-to-head test of various grammar checkers ranging from WordPerfect 8, released in the late 1990s, up to Word 2007. When checked against 20 sentences containing the most common writing errors, all the grammar checkers performed fairly miserably. No version of Word after 2000 caught any of the mistakes (oddly, Word 97 scored better) and WordPerfect only identified 40 percent of the errors. While those numbers don't represent the latest versions of grammar checkers, they do point to one of the biggest challenges in creating a powerful and precise grammar engine that's built into a piece of software - space. Ginés Marín defends Word's precision but admits that space constraints affected the level of "coverage" that Microsoft's grammar checker provided. When the model was slimmed down to fit into the software, it also needed to be dialed back in breadth so that it didn't flag lots of good text as mistakes.
Now engineers don't have to cram a large grammar engine into a package small enough to live on the user's hard drive. The grammar algorithms can live in the cloud and be accessed over the internet in real time. Hendrich says that the web-based versions of Office already rely on robust grammar engines that are hosted in the cloud, and her team is currently in the process of moving all the old built-in critiques and grammar models to the cloud, too. The challenge going forward, says Hendrich, is to decide how much functionality to keep "in the box" and how much to deliver "through the service," as Hendrich calls Microsoft's cloud-based, software-as-a-service model. Every time Word calls up to the cloud for grammar advice, it costs a few fractions of a penny. The latest version of Microsoft's grammar editor is far more robust than its predecessors. Errors come with multiple correction suggestions plus explanations for the grammar rules behind them. There's a built-in read-aloud function that's particularly helpful for people with dyslexia and for non-native speakers.
|
|
