We use a few cookies

Essential cookies keep the library working. With your permission, we'd also like to use analytics to see what's useful and improve the experience. Read our Privacy Policy or Cookie Policy.

Skip to main content
Home/e-Resources/Artificial Intelligence & Machine Learning/Improving Disentangled Text Representation Learning with Information-Theoretic Guidance
Conference ProceedingsArtificial Intelligence & Machine Learning

Improving Disentangled Text Representation Learning with Information-Theoretic Guidance

Checking your access to this record.

Abstract & Overview

Pengyu Cheng, Martin Renqiang Min, Dinghan Shen, Christopher Malon, Yizhe Zhang, Yitong Li, Lawrence Carin. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.

Cite this record

Pengyu Cheng, Martin Renqiang Min, Dinghan Shen, Christopher D. Malon, Yizhe Zhang, Yitong Li, et al. (2020). Improving Disentangled Text Representation Learning with Information-Theoretic Guidance. Annual Meeting of the Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.acl-main.673

Copy in another style

Cheng, P., Min, M. R., Shen, D., Malon, C. D., Zhang, Y., Li, Y., & Carin, L. (2020). Improving Disentangled Text Representation Learning with Information-Theoretic Guidance. Annual Meeting of the Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.acl-main.673
Author names are used as deposited, so check their order before you submit.

Metadata is harvested from open-access indexes and can be incomplete. Check a citation against the published version before relying on it — see our open access & attribution policy.