  Question answering is an important and difficult task in the natural language
processing domain, because many basic natural language processing tasks can be
cast into a question answering task. Several deep neural network architectures
have been developed recently, which employ memory and inference components to
memorize and reason over text information, and generate answers to questions.
However, a major drawback of many such models is that they are capable of only
generating single-word answers. In addition, they require large amount of
training data to generate accurate answers. In this paper, we introduce the
Long-Term Memory Network (LTMN), which incorporates both an external memory
module and a Long Short-Term Memory (LSTM) module to comprehend the input data
and generate multi-word answers. The LTMN model can be trained end-to-end using
back-propagation and requires minimal supervision. We test our model on two
synthetic data sets (based on Facebook's bAbI data set) and the real-world
Stanford question answering data set, and show that it can achieve
state-of-the-art performance.
