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
- Editors:
- Asli Celikyilmaz, Tsung-Hsien Wen
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 224–231
- Language:
- URL:
- https://aclanthology.org/2020.acl-demos.26
- DOI:
- 10.18653/v1/2020.acl-demos.26
- Cite (ACL):
- Xiang Gao, Michel Galley, and Bill Dolan. 2020. MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 224–231, Online. Association for Computational Linguistics.
- Cite (Informal):
- MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform (Gao et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-demos.26.pdf
- Code
- microsoft/MixingBoard