Stochastic text generation
By Jon Oberlander, Chris Brew. 2000
40 citations recorded by the source corpus.
Natural language generation systems must achieve fluency goals, as well as fidelity goals. Fluency helps make systems more usable by, for instance, producing language that is easier for people to process, or which engenders a positive evaluation of the system. Using very simple examples, we have explored one way to achieve specific fluency goals. These goals are stated as norms on ‘macroscopic’ properties of the text as a whole, rather than on individual words or sentences. Such properties are hard to accommodate within a conventional architecture. One solution is a two-component architecture, which permits independent variation of the components, either or both of which can be stochastic.
Subfields: Artificial Intelligence, Natural Language Generation
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Marilyn A. Walker
- François Mairesse
- Verena Rieser
- Amanda Stent
- Rashmi Prasad
- Ondrej Dusek
- Marco Guerini
- Serra Sinem Tekiroğlu
In Natural Language Generation
Citations in the corpus
- Individual and Domain Adaptation in Sentence Planning for Dialogue (2007, background): “• It is common knowledge that individual authors can be identified from the linguistic features of their written texts (Madigan, Genkin, Lewis, Argamon, Fradkin, & Ye, 2005; Oberlander & Brew, 2000).”
- Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020, background): “Thus, we settle on the author-reviewer modular architecture (Oberlander and Brew, 2000; Manurung et al., 2008).”