"Don't worry, human intelligence will never be replaced by machines." That's what I was told as a freshman foreign languages student at a university. That was the time the concerns about the machine translation taking over the human, first came up. For an honest average playgoer, language is nothing but a set of words put in a specific order based on some (not so) simple rules. Learning languages is a grind.

"Don't worry, human intelligence will never be replaced by machines." That's what I was told as a freshman foreign languages student at a university. That was the time the concerns about the machine translation taking over the human, first came up. For an honest average playgoer, language is nothing but a set of words put in a specific order based on some (not so) simple rules. Learning languages is a grind.

Machine learning-based natural language processing systems are amazingly effective, when plentiful labeled training data exists for the task/domain of interest. Unfortunately, for broad coverage (both in task and domain) language understanding, we're unlikely to ever have sufficient labeled data, and systems must find some other way to learn. I'll describe a novel algorithm for learning from interactions, and several problems of interest, most notably machine simultaneous interpretation (translation while someone is still speaking). This is all joint work with some amazing (former) students He He, Alvin Grissom II, John Morgan, Mohit Iyyer, Sudha Rao and Leonardo Claudino, as well as colleagues Jordan Boyd-Graber, Kai-Wei Chang, John Langford, Akshay Krishnamurthy, Alekh Agarwal, Stéphane Ross, Alina Beygelzimer and Paul Mineiro.

MIT researchers have designed a computer system that learns how to play a text-based computer game with no prior assumptions about how language works. Although the system can't complete the game as a whole, its ability to complete sections of it suggests that, in some sense, it discovers the meanings of words during its training. In 2011, professor of computer science and engineering Regina Barzilay and her students reported a system that learned to play a computer game called "Civilization" by analyzing the game manual. But in the new work, on which Barzilay is again a co-author, the machine-learning system has no direct access to the underlying "state" of the game program -- the data the program is tracking and how it's being modified. "When you play these games, every interaction is through text," says Karthik Narasimhan, an MIT graduate student in computer science and engineering and one of the new paper's two first authors.

Mondly, the second-largest free language app available, teaches 33 languages and has been downloaded over ten million times. The developers have made this app even better by creating a new voice Chatbot that makes learning easier through the use of proprietary technology. The Chatbot is interactive, letting people talk in the language they are attempting to learn and respond accordingly with a human voice. CEO and Co-Founder Alexandru Iliescu explains, "The best way to learn a new language is to build your confidence with practice… This new feature is truly a revolutionary new way to learn a new language and we're proud that Mondly is the first to do it." Speech recognition technology lets the Chatbot understand millions of phrases in all of these 33 languages, making it possible to have a conversation.

A pattern recognition algorithm is described that learns a transition net grammar from positive examples. Two sets of examples -- one in English and one in Chinese -- are presented. It is hoped that language learning will reduce the knowledge acquisition effort for expert systems and make the natural language interface to database systems more transportable. The algorithm presented makes a step in that direction by providing a robust parser and reducing special interaction for introduction of new words and terms.