Abstract
The Multilingual Surface Realization Shared Task 2019 focuses on generating sentences from lemmatized sets of universal dependency parses with rich features. This paper describes the results of our participation in the deep track. The core innovation in our approach is to use a graph convolutional network to encode the dependency trees given as input. Upon adding morphological features, our system achieves the third rank without using data augmentation techniques or additional components (such as a re-ranker).- Anthology ID:
- D19-6310
- Volume:
- Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Simon Mille, Anja Belz, Bernd Bohnet, Yvette Graham, Leo Wanner
- Venue:
- WS
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 75–80
- Language:
- URL:
- https://aclanthology.org/D19-6310
- DOI:
- 10.18653/v1/D19-6310
- Cite (ACL):
- Xudong Hong, Ernie Chang, and Vera Demberg. 2019. Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders. In Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019), pages 75–80, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders (Hong et al., 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D19-6310.pdf