Learning to Answer Questions by Understanding Using Entity-Based Memory Network

Xun Wang, Katsuhito Sudoh, Masaaki Nagata, Tomohide Shibata, Daisuke Kawahara, Sadao Kurohashi


This paper introduces a novel neural network model for question answering, the entity-based memory network. It enhances neural networks’ ability of representing and calculating information over along period by keeping records of entities containedin 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. Entities in this model are regard as the basic units that carry information and construct text. Information carried by text are encoded in the states of entities. Hence text can be best understood by analysing its containing 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.


Text comprehension, entity memory network, question answering

Full Text: PDF