Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation
Thiago Castro Ferreira, Iacer Calixto, Sander Wubben, Emiel Krahmer
Abstract
In this paper, we study AMR-to-text generation, framing it as a translation task and comparing two different MT approaches (Phrase-based and Neural MT). We systematically study the effects of 3 AMR preprocessing steps (Delexicalisation, Compression, and Linearisation) applied before the MT phase. Our results show that preprocessing indeed helps, although the benefits differ for the two MT models.- Anthology ID:
- W17-3501
- Volume:
- Proceedings of the 10th International Conference on Natural Language Generation
- Month:
- September
- Year:
- 2017
- Address:
- Santiago de Compostela, Spain
- Editors:
- Jose M. Alonso, Alberto Bugarín, Ehud Reiter
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–10
- Language:
- URL:
- https://aclanthology.org/W17-3501
- DOI:
- 10.18653/v1/W17-3501
- Cite (ACL):
- Thiago Castro Ferreira, Iacer Calixto, Sander Wubben, and Emiel Krahmer. 2017. Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation. In Proceedings of the 10th International Conference on Natural Language Generation, pages 1–10, Santiago de Compostela, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation (Castro Ferreira et al., INLG 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/W17-3501.pdf