Optimising text quality in generation from relational databases
By Mick O'Donnell, A. Knott, Jon Oberlander, C. Mellish. 2000
18 citations recorded by the source corpus.
This paper outlines a text generation system suited to a large class of information sources, relational databases. We focus on one aspect of the problem: the additional information which needs to be specified to produce reasonable text quality when generating from relational databases. We outline how databases need to be prepared, and then describe various types of domain semantics which can be used to improve text quality.
Subfields: Artificial Intelligence, Natural Language Processing, 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
- Caiming Xiong
- Richard Socher
- Mohit Bansal
- Dragomir R. Radev
- Luke S. Zettlemoyer
- Percy Liang
- Tao Yu
- Jonathan Berant
In Natural Language Processing
- Caiming Xiong
- Dragomir R. Radev
- Richard Socher
- Tao Yu
- Rui Zhang
- Mohit Bansal
- Xi Victoria Lin
- Luke S. Zettlemoyer
In Natural Language Generation
Citations in the corpus
- DART: Open-Domain Structured Data Record to Text Generation (2020, background): “, 2018; Perez-Beltrachini and Lapata, 2018), knowledge-base descriptions (OâĂŹDonnell et al., 2000; Banik et al., 2013; Gardent et al., 2017a,b; Novikova et al., 2017b; Wang et al., 2018; Yu et al., 2019), code comment (Iyer et al.”
- Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence (2019, methodology): “[44] designed a text generation system by utilizing the potential rules from relational databases.”
- ESPRIT: Explaining Solutions to Physical Reasoning Tasks (2020, background): “, 2018; Perez-Beltrachini and Lapata, 2018), descriptions of knowledge bases (ODonnell et al., 2000; Trisedya et al., 2018; Zhu et al., 2019; Yu et al., 2019) and source code (Iyer et al.”