Learning to Answer Questions by Understanding Using Entity-Based Memory Network
Título del documento: Learning to Answer Questions by Understanding Using Entity-Based Memory Network
Revue: Computación y sistemas
Base de datos: PERIÓDICA
Número de sistema: 000423287
ISSN: 1405-5546
Autores: 1
2
1
3
3
3
Instituciones: 1Nippon Telegraph and Telephone Coorporation, Tokio. Japón
2Nara Institute of Science and Technology, Nara. Japón
3Kyoto University, Kyoto. Japón
Año:
Periodo: Oct-Dic
Volumen: 21
Número: 4
País: México
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Aplicado, descriptivo
Resumen en inglés 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 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. 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
Disciplinas: Ciencias de la computación
Palabras clave: Inteligencia artificial,
Comprensión de textos,
Entidad de red,
Memoria,
Búsqueda de respuestas
Keyword: Artificial intelligence,
Memory,
Text comprehension,
Network entity,
Question answering
Texte intégral: Texto completo (Ver HTML) Texto completo (Ver PDF)