| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560413 |
| ISSN: | 1405-5546 |
| Autores: | Costa jussà, Marta R1 Nuez, Álvaro1 Segura, Carlos2 |
| Instituciones: | 1Universitat Politécnica de Valencia, Valencia. España 2Telefònica I+D, Barcelona. España |
| Año: | 2018 |
| Periodo: | Oct-Dic |
| Volumen: | 22 |
| Número: | 4 |
| Paginación: | 1233-1239 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Chatbots aim at automatically offering a conversation between a human and a computer. While there is a long track of research in rule-based and retrieval-based approaches, the generation-based approaches are promisingly emerging solving issues like responding to queries in inference that were not previously seen in development or training time. In this paper, we offer an experimental view of how recent advances in close areas as machine translation can be adopted for chatbots. In particular, we compare how alternative encoder-decoder deep learning architectures perform in the context of chatbots. Our research concludes that a fully attention-based architecture is able to outperform the recurrent neural network baseline system. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Chatbot, Encoder-decoder, Attention mechanisms, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
