University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task

Ratish Puduppully, Jonathan Mallinson, Mirella Lapata


Abstract
The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages. For the NLG track, we submitted a multilingual system based on the Content Selection and Planning model of Puduppully et al (2019). For the MT track, we submitted Transformer-based Neural Machine Translation models, where out-of-domain parallel data was augmented with in-domain data extracted from monolingual corpora. Our MT+NLG systems disregard the structured input data and instead rely exclusively on the source summaries.
Anthology ID:
D19-5630
Volume:
Proceedings of the 3rd Workshop on Neural Generation and Translation
Month:
November
Year:
2019
Address:
Hong Kong
Venue:
NGT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
268–272
Language:
URL:
https://aclanthology.org/D19-5630
DOI:
10.18653/v1/D19-5630
Bibkey:
Cite (ACL):
Ratish Puduppully, Jonathan Mallinson, and Mirella Lapata. 2019. University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task. In Proceedings of the 3rd Workshop on Neural Generation and Translation, pages 268–272, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task (Puduppully et al., NGT 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/D19-5630.pdf
Code
 ratishsp/data2text-table-plan-py