Reading Comprehension using Entity-based Memory Network
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2016
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| _version_ | 1866914688697106432 |
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| author | Wang, Xun Sudoh, Katsuhito Nagata, Masaaki Shibata, Tomohide Kawahara, Daisuke Kurohashi, Sadao |
| author_facet | Wang, Xun Sudoh, Katsuhito Nagata, Masaaki Shibata, Tomohide Kawahara, Daisuke Kurohashi, Sadao |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_1612_03551 |
| institution | arXiv |
| publishDate | 2016 |
| record_format | arxiv |
| spellingShingle | Reading Comprehension using Entity-based Memory Network Wang, Xun Sudoh, Katsuhito Nagata, Masaaki Shibata, Tomohide Kawahara, Daisuke Kurohashi, Sadao Computation and Language Artificial Intelligence 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. |
| title | Reading Comprehension using Entity-based Memory Network |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/1612.03551 |