During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.

What are the main differences between NLP for Chinese vs NLP for English? Linguistically speaking, Chinese is an isolating language different from English. There are no spaces between words in Chinese written texts, and Chinese grammatical relations are indicated by word order. These factors have multiplied the difficulty of Chinese disambiguation at lexical, syntactic and semantic levels, since modern linguistic concepts and principles are more suitable for English than for Chinese. Currently, most mainstream NLP methods are language-independent.

Android phone users generally have a stock Android keyboard on their phones unless they opt for a third-party one. Google's new Gboard update will add some new features to the stock keyboard and make real-time translation easy, along with adding new Emojis and GIF features. Language translation on a smartphone needed users to use a third-party app or navigate to Google translate. However, with the new integration, users can simply type a phrase and have it translated in the subject box as the keyboard will automatically make suggestions to translate it into the default language chosen in the settings, Google said in its official blog. The new update will give voice-typing a new dimension.

Planning to Netflix and chill this weekend? The movie you choose to watch may be heavily influenced by Netflix's sophisticated algorithms. Similarly, decisions like where you choose to dine and what you choose to wear are increasingly facilitated by predictive technologies powered by deep learning. Here are five ways that popular consumer tech companies Netflix, Yelp, Yahoo, StitchFix, and Google improve your online experience with artificial intelligence. Historically, watching television is a unidirectional communication channel.

Whether an A.I. ought to be granted patent rights is a timely question given the increasing proliferation of A.I. in the workplace. One might argue that Intellectual Property (IP) laws and IP Rights were designed to exclusively benefit human creators and inventors[7] and thus would exclude non-humans from holding IP rights. However, many IP laws were drafted well before the emergence of A.I. and in any case, do not explicitly require that a creator or inventor be'human.' The World Intellectual Property Organization's (WIPOs) definition of Intellectual Property talks about creations of the mind[9] but does not specify whether it must be a human mind. Similarly, provisions in laws promoting innovation and IP rights, such as the so-called Intellectual Property Clause of the U.S. Constitution[10], also do not explicitly mention a'human' requirement.

Whether or not an artificial intelligence (AI) ought to be granted patent rights is a timely dilemma given the increasing proliferation of AI in the workplace. AI technology has been applied effectively in medical advancements, psycholinguistics, and tourism and food preparation. Even a film written by an AI recently debuted online, and AI has sneaked into the legal profession. One might argue that Intellectual Property (IP) laws and IP Rights were designed to excusively benefit human creators and inventors, and thus would exclude non-humans from holding IP rights. However, many IP laws were drafted well before the emergence of AI, and in any case do not explicitly require that a creator or inventor be'human.'

Do conservatives and liberals speak different languages? Given the heated nature of political debate these days, it certainly seems like it. But how do you find common ground when people from different sides of the aisle are trying to communicate? This question was one of the motivations behind Partisan Thesaurus, a project that enables you to type any word and see the other words with which liberals and conservatives mostly commonly associate it. Type a politically charged word like "immigrant," and see that liberals associate it with "undocumented," "innocent," and "unarmed," while conservatives associate it with "alien," "outcast," and "obscure."

Late last year, Google announced a breakthrough in translating. Dubbed Neural Machine Translation, it let Google's AI-powered engine tackle full sentences instead of just words, giving translations a more natural feel. Now Google is expanding it to a several more languages. Back when it launched, Google's new translator was available for English and just eight other languages, including French, German, Spanish, Portuguese, Chinese, Japanese, Korean, and Turkish. Now it is bringing it to Hindi, Russian, and Vietnamese, with more rolling out in the coming weeks.

Teaching machines to truly understand natural language has been one of the biggest challenges facing computer scientists working to advance artificial intelligence. But Google has made real progress in getting computers to look at language as more than just a bag of words, and these advancements are now making their way into its products. Google Translate, for example, is getting a technical makeover with the introduction of Neural Machine Translation (NMT). This follows the first go at utilizing NMT in Translate last November, when English, French, German, Spanish, Portuguese, Chinese, Japanese, Korean and Turkish all saw the same improvement. "We have 103 languages overall, and our goal is to get all of them working with neural nets," a Google spokesperson told the Observer.

GNMT produces higher quality translations than its phrase-based counterparts because it considers the entire sentence instead of breaking it up. It takes each character and compares it to those that come before and after. This is important to deciphering languages like Mandarin Chinese, for example, where words can mean different things based on which characters they're paired with. Google began using neural machine translation with Mandarin Chinese last year, then expanded support to include English, French, German, Japanese, Korean, Portuguese and Turkish. Around one-third of the world's population speaks these languages, TechCrunch notes, and they make up more than 35 percent of all Google Translate queries. Eventually, Google plans to use neural machine translation for all 103 languages in the app.