Google's PAWS data set helps AI models capture word order and structure

Natural language processing (NLP) -- the AI subfield dealing with machine reading comprehension -- isn't by any stretch solved, and that's because syntactic nuances can enormously impact the meaning of a sentence or phrase. Consider the paraphrase pairs "Flights from New York to Florida" and "Flights to Florida from New York," which convey the same thing. Even state-of-the-art algorithms fail to distinguish them from a snippet like "Flights from Florida to New York," which has a different meaning. Google thinks a greater diversity of data is one of the keys to solving hard NLP problems, and to this end, it's today releasing two new corpora: Paraphrase Adversaries from Word Scrambling (PAWS) in English. Both data sets contain "well-formed" pairs of paraphrases and non-paraphrases, which Google says can improve algorithmic accuracy in capturing word order and structure from below 50% to between 85% and 89%.