Total correlation variational autoencoder strengthened with attentions for segmenting syntax and semantics
US11748567B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 10, 2020 |
| Grant date | Sep 5, 2023 |
| Priority date | — |
| Expiry date | Aug 7, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/044
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Described herein are embodiments of a framework named as total correlation variational autoencoder (TC_VAE) to disentangle syntax and semantics by making use of total correlation penalties of KL divergences. One or more Kullback-Leibler (KL) divergence terms in a loss for a variational autoencoder are discomposed so that generated hidden variables may be separated. Embodiments of the TC_VAE framework were examined on semantic similarity tasks and syntactic similarity tasks. Experimental results show that better disentanglement between syntactic and semantic representations have been achieved compared with state-of-the-art (SOTA) results on the same data sets in similar settings.
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