 Hello, I am Varavi. I am a PhD student at the Institute of Pediatrics. I am working with Dr. John McCray and Dr. Mihail Erkan. My PhD thesis is on machine translation of under-resourced languages. Now I am going to tell about some scenario where my research will be used. We take a scenario where my friend Brandon, he speaks only English. He goes to an island where the people speak only Telugu. He travels around the world for research as well as work as well as leisure. But when he landed on the island, he realized nobody speaks English there. Now he is in medical attention, but he finds out that when he goes to the medical centre, he couldn't find anybody who speaks English. Even the nurse speaks also the local language. So as everybody else, he takes out his mobile and tries to check out the language for translation. But he couldn't find the language Telugu. The reason for this is a computer program requires millions of examples of translations to create a translation system like Google Translate. But for many languages, it doesn't available. There are around 7000 languages in the world. Only a few languages like 80 languages have translation in a very popular Google translation like Google Translate. The reason is those languages doesn't have much resources. So in my research, we try to combine the languages with the highly resourced languages which are closely related. And then we use those resources for creating the translations. So the first step is combining those languages based on the language family. The reason behind this, if you speak, if you see Danish, Naroen and Scandinavian people come together, they can speak to each other without the knowledge of the other language. Since all these three languages are closely related and they have same language ancestors. But there is another problem arises for the languages which uses different scripts even though they belong to the same language family. So in our approach, we try to bring them to a single script and then use those all the translation examples from the closely related languages to build the machine translation systems. So our program finally will be able to translate between 7000 languages of the world languages. So the usage of this would be like now Brandon can go anywhere for his work this year and then he doesn't need to worry about the language barrier and he can collaborate with many people as many people as possible and he doesn't need to worry about the medical attention. For the people in the local language, the minority communities, they can also be able to collaborate with the world and they can be able to use the resources from the other languages by translating to their languages. And finally the nurse, she can go online shopping and without worrying about the bank details because it is in our own language now she will be able to trust it and she will be able to know what to do without trying to guess. Thanks.