Event Representation with Sequential, Semi-Supervised Discrete Variables
| dc.contributor.author | Rezaee, Mehdi | |
| dc.contributor.author | Ferraro, Francis | |
| dc.date.accessioned | 2021-04-05T17:32:27Z | |
| dc.date.available | 2021-04-05T17:32:27Z | |
| dc.date.issued | 2021-06-06 | |
| dc.description | Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies | |
| dc.description.abstract | Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential, neural variational autoencoder that uses a carefully defined encoder, and Gumbel-Softmax reparametrization, to allow for successful backpropagation during training. We show that our approach outperforms multiple baselines and the state-of-the-art in narrative script induction on multiple event modeling tasks. We demonstrate that our approach converges more quickly. | en_US |
| dc.description.sponsorship | We would also like to thank the anonymous reviewers for their comments, questions, and suggestions. Some experiments were conducted on the UMBC HPCF, supported by the National Science Foundation under Grant No. CNS-1920079. We’d also like to thank the reviewers for their comments and suggestions. This material is based in part upon work supported by the National Science Foundation under Grant Nos. IIS-1940931 and IIS2024878. This material is also based on research that is in part supported by the Air Force Research Laboratory (AFRL), DARPA, for the KAIROS program under agreement number FA8750-19-2-1003. The U.S.Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either express or implied, of the Air Force Research Laboratory (AFRL), DARPA, or the U.S. Government. | |
| dc.description.uri | https://aclanthology.org/2021.naacl-main.374/ | en_US |
| dc.format.extent | 10 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.identifier | doi:10.13016/m25kvm-4wa0 | |
| dc.identifier.citation | Rezaee, Mehdi; Ferraro, Francis; Event Representation with Sequential, Semi-Supervised Discrete Variables; Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 6 June, 2021; https://aclanthology.org/2021.naacl-main.374/ | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/21283 | |
| dc.identifier.uri | http://dx.doi.org/10.18653/v1/2021.naacl-main.374 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Association for Computational Linguistics | |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | en_US |
| dc.subject | UMBC Ebiquity Research Group | |
| dc.title | Event Representation with Sequential, Semi-Supervised Discrete Variables | en_US |
| dc.type | Text | en_US |