Abstract
A DAG automaton is a formal device for manipulating graphs. By augmenting a DAG automaton with transduction rules, a DAG transducer has potential applications in fundamental NLP tasks. In this paper, we propose a novel DAG transducer to perform graph-to-program transformation. The target structure of our transducer is a program licensed by a declarative programming language rather than linguistic structures. By executing such a program, we can easily get a surface string. Our transducer is designed especially for natural language generation (NLG) from type-logical semantic graphs. Taking Elementary Dependency Structures, a format of English Resource Semantics, as input, our NLG system achieves a BLEU-4 score of 68.07. This remarkable result demonstrates the feasibility of applying a DAG transducer to resolve NLG, as well as the effectiveness of our design.- Anthology ID:
- P18-1179
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1928–1937
- Language:
- URL:
- https://aclanthology.org/P18-1179
- DOI:
- 10.18653/v1/P18-1179
- Cite (ACL):
- Yajie Ye, Weiwei Sun, and Xiaojun Wan. 2018. Language Generation via DAG Transduction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1928–1937, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Language Generation via DAG Transduction (Ye et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/P18-1179.pdf