Abstract--When the amount of parallel sentences available to train a neural machine translation is scarce, a common practice is to generate new synthetic training samples from them. A number of approaches have been proposed to produce synthetic parallel sentences that are similar to those in the parallel data available. These approaches work under the assumption that non-fluent target-side synthetic training samples can be harmful and may deteriorate translation performance. Even so, in this paper we demonstrate that synthetic training samples with non-fluent target sentences can improve translation performance if they are used in a multilingual machine translation framework as if they were sentences in another language. We conducted experiments on ten low-resource and four high-resource translation tasks and found out that this simple approach consistently improves translation performance as compared to state-of-the-art methods for generating synthetic training samples similar to those found in corpora. Furthermore, this improvement is independent of the size of the original training corpus, the resulting systems are much more robust against domain shift and produce less hallucinations.

As technology becomes a more intimate part of our everyday lives, artificial intelligence is driving progress and helping solve some of society's greatest challenges. BI Norwegian Business School is researching the impact of technology and artificial intelligence on sustainability, gender equality, health and wellbeing, justice, social responsibility, and responsible investment and education. Matilda Dorotic, an associate professor in marketing at the business school, has been involved in extensive research on the impact of technology and big data on diverse aspects of well-being, civil-mindedness, and smart cities--she's recently been invited by the European Commission to talk about the societal issues of implementing artificial intelligence and its far-reaching impact on citizens. It's something we all need to pay more attention to as, according to Matilda "citizens know so little about the ways in which artificial intelligence is fundamentally changing their everyday lives". According to the WHO, cancer is the second leading cause of death worldwide and is responsible for around 10 million deaths per year.

For candidates who interact with Matilda the experience can seem bizarre -- being interviewed by a toy. But remember that Matilda is a prototype. It is a social robot -- an autonomous device that interacts and communicates with humans by following social behaviors and rules attached to its role. There are many like it, including Jibo, ElliQ, and Pepper, though it is the first that is being used in recruiting. There's no compelling reason it has to exist in physical form to interview candidates, other than that it was available and could be adapted for use in screening candidates. Humanoid robots may very well be commonplace in the next 20 years, but they're not needed for recruiting.

Their information needs are long and popular information needs of the masses. Topic term and highly dynamic - nearly everything about this topic specificity, customizability, and automatically pursuing the is in flux. For these users, information search can be made long term unique information needs of individual users are more effective with knowledge about the field and about the not among the strengths of current main stream search engines types of documents being retrieved. Because the resource (Jansen, Spink, and Saracevic 2000) (Teevan, Dumais, management decisions require judgment about the materials and Horvitz 2005). This gap has inspired web personalization collected, the users require confidentiality and must trust the and collaborative information seeking tools such as sources. Google Alerts and has encouraged topic-specific blogs and Matilda is designed to 1) tailor information collection for podcasts.