SimpleNLG-ZH: a Linguistic Realisation Engine for Mandarin

By Guanyi Chen, Kees van Deemter, Chenghua Lin. 2018

11 citations recorded by the source corpus.

We introduce SimpleNLG-ZH, a realisation engine for Mandarin that follows the software design paradigm of SimpleNLG (Gatt and Reiter, 2009). We explain the core grammar (morphology and syntax) and the lexicon of SimpleNLG-ZH, which is very different from English and other languages for which SimpleNLG engines have been built. The system was evaluated by regenerating expressions from a body of test sentences and a corpus of human-authored expressions. Human evaluation was conducted to estimate the quality of regenerated sentences.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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

1. Filip Jurvc'ivcek
2. Ondrej Dusek
3. Verena Rieser
4. Jekaterina Novikova
5. Yevgeniy Puzikov
6. Zdeněk Kasner
7. Sebastian Gehrmann
8. Ehud Reiter

In Computer Vision and Pattern Recognition

1. Filip Jurvc'ivcek
2. Ondrej Dusek
3. Verena Rieser

In Natural Language Processing

1. Filip Jurvc'ivcek
2. Ondrej Dusek
3. Verena Rieser
4. Mihir Kale
5. Jekaterina Novikova
6. Abhinav Rastogi
7. Yevgeniy Puzikov
8. Zdeněk Kasner

Citations in the corpus

• Neural Generation for Czech: Data and Baselines (2019, background): “…procedural Sim-pleNLG realizer (Gatt and Reiter, 2009) has also been ported into multiple languages (Bollmann, 2011; Vaudry and Lapalme, 2013; de Oliveira and Sripada, 2014; Mazzei et al., 2016; Ramos-Soto et al., 2017; Cascallar-Fuentes et al., 2018; Chen et al., 2018; de Jong and Theune, 2018).”