Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment
| dc.contributor.author | Ganesan, Ashwinkumar | |
| dc.contributor.author | Ferraro, Francis | |
| dc.contributor.author | Oates, Tim | |
| dc.date.accessioned | 2021-08-06T18:22:15Z | |
| dc.date.available | 2021-08-06T18:22:15Z | |
| dc.date.issued | 2021-08-01 | |
| dc.description | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, August 1–6, 2021 | en_US |
| dc.description.abstract | We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective. We demonstrate BDMA by training a model for a pair of languages rather than individual, directed source and target combinations, reducing the number of models by 50%. We show that models trained with BDMA in the “forward” (source to target) direction can successfully map words in the “reverse” (target to source) direction, yielding equivalent (or better) performance to standard unidirectional translation models where the source and target language is flipped. We also show how BDMA reduces the overall size of the model. | en_US |
| dc.description.sponsorship | We thank our anonymous reviewers for their constructive reviews and the UMBC HPCF facility for computational resources. This work is in part supported by 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. | en_US |
| dc.description.uri | https://aclanthology.org/2021.findings-acl.276/ | en_US |
| dc.format.extent | 8 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.identifier | doi:10.13016/m2thlk-td7q | |
| dc.identifier.citation | Ganesan, Ashwinkumar; Ferraro, Francis; Oates, Tim; Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment; Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 3132–3139 August 1–6, 2021; https://aclanthology.org/2021.findings-acl.276.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/22331 | |
| dc.identifier.uri | http://dx.doi.org/10.18653/v1/2021.findings-acl.276 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Association for Computational Linguistics | en_US |
| 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. | en_US |
| dc.subject | UMBC Ebiquity Research Group | en_US |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | |
| dc.title | Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment | en_US |
| dc.type | Text | en_US |