CONSULT=OCASNLUS.how is ADVICED written in same code language?
Answers
Answered by
0
The work may be of some help to programmers, and it could let nonprogrammers manipulate common types of files -- like word-processing documents and spreadsheets -- in ways that previously required familiarity with programming languages. But the researchers' methods could also prove applicable to other programming tasks, expanding the range of contexts in which programmers can specify functions using ordinary language.
"I don't think that we will be able to do this for everything in programming, but there are areas where there are a lot of examples of how humans have done translation," says Regina Barzilay, an associate professor of computer science and electrical engineering and a co-author on both papers. "If the information is available, you may be able to learn how to translate this language to code."
In other cases, Barzilay says, programmers may already be in the practice of writing specifications that describe computational tasks in precise and formal language. "Even though they're written in natural language, and they do exhibit some variability, they're not exactly Shakespeare," Barzilay says. "So again, you can translate them."
The researchers' recent papers demonstrate both approaches. In work presented in June at the annual Conference of the North American Chapter of the Association for Computational Linguistics, Barzilay and graduate student Nate Kushman used examples harvested from the Web to train a computer system to convert natural-language descriptions into so-called "regular expressions": combinations of symbols that enable file searches that are far more flexible than the standard search functions available in desktop software.
In a paper being presented at the Association for Computational Linguistics' annual conference in August, Barzilay and another of her graduate students, Tao Lei, team up with professor of electrical engineering and computer science Martin Rinard and his graduate student Fan Long to describe a system that automatically learned how to handle data stored in different file formats, based on specifications prepared for a popular programming competition.
Regular irregularities
As Kushman explains, computer science researchers have had some success with systems that translate questions written in natural language into special-purpose formal languages -- languages used to specify database searches, for instance. "Usually, the way those techniques work is that they're finding some fairly direct mapping between the natural language and this formal representation," Kushman says. "In general, the logical forms are handwritten so that they have this nice mapping."
Unfortunately, Kushman says, that approach doesn't work with regular expressions, strings of symbols that can describe the data contained in a file with great specificity. A regular expression could indicate, say, just those numerical entries in a spreadsheet that are three columns over from a cell containing a word of any length whose final three letters are "BOS."
But regular expressions, as ordinarily written, don't map well onto natural language. For example, Kushman explains, the regular expression used to search for a three-letter word starting with "a" would contain a symbol indicating the start of a word, another indicating the letter "a," a set of symbols indicating the identification of a letter, and a set of symbols indicating that the previous operation should be repeated twice. "If I'm trying to do the same syntactic mapping that I would normally do," Kushman says, "I can't pull out any sub-chunk of this that means 'three-letter.'"
What Kushman and Barzilay determined, however, is that any regular expression has an equivalent that does map nicely to natural language -- although it may not be very succinct or, for a programmer, very intuitive. Moreover, using a mathematical construct known as a graph, it's possible to represent all equivalent versions of a regular expression at once. Kushman and Barzilay's system thus has to learn only one straightforward way of mapping natural language to symbols; then it can use the graph to find a more succinct version of the same expression.
When Kushman presented the paper he co-authored with Barzilay, he asked the roomful of computer scientists to write down the regular expression corresponding to a fairly simple text search. When he revealed the answer and asked how many had gotten it right, only a few hands went up. So the system could be of use to accomplished programmers, but it could also allow casual users of, say, spreadsheet and word-processing programs to specify elaborate searches using natural language.
"I don't think that we will be able to do this for everything in programming, but there are areas where there are a lot of examples of how humans have done translation," says Regina Barzilay, an associate professor of computer science and electrical engineering and a co-author on both papers. "If the information is available, you may be able to learn how to translate this language to code."
In other cases, Barzilay says, programmers may already be in the practice of writing specifications that describe computational tasks in precise and formal language. "Even though they're written in natural language, and they do exhibit some variability, they're not exactly Shakespeare," Barzilay says. "So again, you can translate them."
The researchers' recent papers demonstrate both approaches. In work presented in June at the annual Conference of the North American Chapter of the Association for Computational Linguistics, Barzilay and graduate student Nate Kushman used examples harvested from the Web to train a computer system to convert natural-language descriptions into so-called "regular expressions": combinations of symbols that enable file searches that are far more flexible than the standard search functions available in desktop software.
In a paper being presented at the Association for Computational Linguistics' annual conference in August, Barzilay and another of her graduate students, Tao Lei, team up with professor of electrical engineering and computer science Martin Rinard and his graduate student Fan Long to describe a system that automatically learned how to handle data stored in different file formats, based on specifications prepared for a popular programming competition.
Regular irregularities
As Kushman explains, computer science researchers have had some success with systems that translate questions written in natural language into special-purpose formal languages -- languages used to specify database searches, for instance. "Usually, the way those techniques work is that they're finding some fairly direct mapping between the natural language and this formal representation," Kushman says. "In general, the logical forms are handwritten so that they have this nice mapping."
Unfortunately, Kushman says, that approach doesn't work with regular expressions, strings of symbols that can describe the data contained in a file with great specificity. A regular expression could indicate, say, just those numerical entries in a spreadsheet that are three columns over from a cell containing a word of any length whose final three letters are "BOS."
But regular expressions, as ordinarily written, don't map well onto natural language. For example, Kushman explains, the regular expression used to search for a three-letter word starting with "a" would contain a symbol indicating the start of a word, another indicating the letter "a," a set of symbols indicating the identification of a letter, and a set of symbols indicating that the previous operation should be repeated twice. "If I'm trying to do the same syntactic mapping that I would normally do," Kushman says, "I can't pull out any sub-chunk of this that means 'three-letter.'"
What Kushman and Barzilay determined, however, is that any regular expression has an equivalent that does map nicely to natural language -- although it may not be very succinct or, for a programmer, very intuitive. Moreover, using a mathematical construct known as a graph, it's possible to represent all equivalent versions of a regular expression at once. Kushman and Barzilay's system thus has to learn only one straightforward way of mapping natural language to symbols; then it can use the graph to find a more succinct version of the same expression.
When Kushman presented the paper he co-authored with Barzilay, he asked the roomful of computer scientists to write down the regular expression corresponding to a fairly simple text search. When he revealed the answer and asked how many had gotten it right, only a few hands went up. So the system could be of use to accomplished programmers, but it could also allow casual users of, say, spreadsheet and word-processing programs to specify elaborate searches using natural language.
Similar questions