Recently, the Question Answering dataset Stanford Question Answering Dataset (SQuAD) has gained a lot of attention from practitioners and researchers due its appealing properties for evaluating the capabilities of agents able to answer open domain questions. In this dataset, given a reference context and a question, the agent should be able to generate an answer which may be composed by multiple tokens which are present in the given context. Due to its high quality, it represents a relevant benchmark for intelligent agents able to grasp, from a given context, relevant evidences that are required to generate the answer. The SQuAD dataset contains questions extracted from Wikipedia and related to specific entities. If the agent is able to extract from the related context text the sequence of tokens which compose the answer we may legitimately state that the system demonstrate sound reasoning capabilities. Obviously, the system should be able to generate an answer without exploiting supplementary features associated to the question or to the context but it should be able to "read" from the context text the correct answer.