GenNI: Human-AI Collaboration for Data-Backed Text Generation
Hendrik Strobelt, Jambay Kinley, Robert Krüger, Johanna Beyer, Hanspeter Pfister, Alexander M. Rush
摘要
Table2Text systems generate textual output based on structured data utilizing machine learning. These systems are essential for fluent natural language interfaces in tools such as virtual assistants; however, left to generate freely these ML systems often produce misleading or unexpected outputs. GenNI (Generation Negotiation Interface) is an interactive visual system for high-level human-AI collaboration in producing descriptive text. The tool utilizes a deep learning model designed with explicit control states. These controls allow users to globally constrain model generations, without sacrificing the representation power of the deep learning models. The visual interface makes it possible for users to interact with AI systems following a Refine-Forecast paradigm to ensure that the generation system acts in a manner human users find suitable. We report multiple use cases on two experiments that improve over uncontrolled generation approaches, while at the same time providing fine-grained control. A demo and source code are available at https://genni.vizhub.ai.
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- Neural Data-to-Text Generation via Jointly Learning the Segmentation and CorrespondenceXiaoyu Shen, Ernie Chang, Hui Su, Cheng Niu 等ACL 2020 · 被引用 46 次
- TaPas: Weakly Supervised Table Parsing via Pre-trainingJonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno 等ACL 2020 · 被引用 19 次
- CoCon: A Self-Supervised Approach for Controlled Text GenerationAlvin Chan, Yew-Soon Ong, Bill Pung, Aston Zhang 等ICLR 2021 · 被引用 16 次
- Latent Template Induction with Gumbel-CRFsYao Fu, Chuanqi Tan, Bin Bi, Mosha Chen 等NeurIPS 2020 · 被引用 15 次
- Posterior Control of Blackbox GenerationXiang Lisa Li, Alexander M. RushACL 2020 · 被引用 2 次
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