LSTMs and Dynamic Memory Networks for Human Written Simple Question Answering
One of the larger goals of Artificial Intelligence research is to produce methods that improve natural language processing and understanding and increase the ability of agents to interact and converse with humans. This by its very nature is an incredibly complex task, and involves myriad subproblems. Many of these problems have been posed and solved successfully, but one of the most interesting problems which is an active area of research currently is developing agents with the ability to answer questions by chaining facts, using inductive or deductive reasoning, parsing the question and looking up the answer, etc. We argue that improving performance on tasks which measure an agents ability to answer questions is an excellent benchmark for further, more general AI improvement. Our problem is therefore built around these tasks. We use modern deep learning techniques to demonstrate increased performance on a variety of canonical question answering tasks. Practically speaking, the Question Answering problem is critical in a very direct way to projects like IBM's Watson[9] which was initially designed to answer trivia questions and is now being applied in the medical field. But it is also important to almost any project or business that requires any type of query, like search engines, chat applications, database programs, etc. Natural language queries are much more common, and much easier for humans to formulate, than other kinds of queries which can more directly access facts such as those written in database languages. It is thus one of the most important problems in the Artificial Intelligence fields to develop models and methods to parse these questions and correctly answer them.