Spreading of mis- and disinformation is growing and is having a big impact on interpersonal communications, politics and even science. Traditional methods, e.g. manual fact-checking by reporters cannot keep up with the growth of information. On the other hand, there has been much progress in natural language processing recently, partly due to the resurgence of neural methods. How can natural language processing methods fill this gap and help to automatically check facts? This talk will explore different ways to frame fact checking and detail our ongoing work on learning to encode documents for automated fact checking, as well as describe future challenges.