Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders

Xudong Hong, Ernie Chang, Vera Demberg


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
Venues:
EMNLP | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
75–80
Language:
URL:
https://www.aclweb.org/anthology/D19-6310
DOI:
10.18653/v1/D19-6310
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-6310.pdf