Reading Comprehension using Entity-based Memory Network
This work addresses reading comprehension for AI systems, but it appears incremental as it builds on previous memory network models with a focus on fine-grained entity handling.
The paper tackles the problem of question answering by introducing an entity-based memory network that enhances neural networks' ability to handle long-term information by tracking entities in text, reporting satisfying experimental results.
This paper introduces a novel neural network model for question answering, the \emph{entity-based memory network}. It enhances neural networks' ability of representing and calculating information over a long period by keeping records of entities contained in text. The core component is a memory pool which comprises entities' states. These entities' states are continuously updated according to the input text. Questions with regard to the input text are used to search the memory pool for related entities and answers are further predicted based on the states of retrieved entities. Compared with previous memory network models, the proposed model is capable of handling fine-grained information and more sophisticated relations based on entities. We formulated several different tasks as question answering problems and tested the proposed model. Experiments reported satisfying results.