Abstract
Context modeling helps understand the data, such as sentence or user behavior. Contextual information captures the important underlying feature, and it enhances the relationship between data instances or hidden representations. As the importance of the sequential model grows, so does the importance of the sequential contextual modeling. Under the sequential data, we need to consider the context change over time. In this paper, we present our research works on context modeling and its dynamics modeling over time. Furthermore, we extend our research to handle the multi-granularity of sequential context modeling to consider rich context representations.
| Original language | English |
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| Title of host publication | Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 |
| Editors | Christian Bessiere |
| Publisher | International Joint Conferences on Artificial Intelligence |
| Pages | 5208-5209 |
| Number of pages | 2 |
| ISBN (Electronic) | 9780999241165 |
| Publication status | Published - 2020 |
| Event | 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan Duration: 2021 Jan 1 → … |
Publication series
| Name | IJCAI International Joint Conference on Artificial Intelligence |
|---|---|
| Volume | 2021-January |
| ISSN (Print) | 1045-0823 |
Conference
| Conference | 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 |
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| Country/Territory | Japan |
| City | Yokohama |
| Period | 21/1/1 → … |
Bibliographical note
Publisher Copyright:© 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence