MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform

Xiang Gao, Michel Galley, Bill Dolan


Abstract
We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation. We unify existing text generation algorithms in a shared codebase and further adapt earlier algorithms for constrained generation. To borrow advantages from different models, we implement strategies for cross-model integration, from the token probability level to the latent space level. An interface to external knowledge is provided via a module that retrieves, on-the-fly, relevant knowledge from passages on the web or a document collection. A user interface for local development, remote webpage access, and a RESTful API are provided to make it simple for users to build their own demos.
Anthology ID:
2020.acl-demos.26
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
224–231
URL:
https://www.aclweb.org/anthology/2020.acl-demos.26
DOI:
Bib Export formats:
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PDF:
https://www.aclweb.org/anthology/2020.acl-demos.26.pdf

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