Selecting, Planning, and Rewriting: A Modular Approach for Data-to-Document Generation and Translation

Lesly Miculicich, Marc Marone, Hany Hassan


Abstract
In this paper, we report our system submissions to all 6 tracks of the WNGT 2019 shared task on Document-Level Generation and Translation. The objective is to generate a textual document from either structured data: generation task, or a document in a different language: translation task. For the translation task, we focused on adapting a large scale system trained on WMT data by fine tuning it on the RotoWire data. For the generation task, we participated with two systems based on a selection and planning model followed by (a) a simple language model generation, and (b) a GPT-2 pre-trained language model approach. The selection and planning module chooses a subset of table records in order, and the language models produce text given such a subset.
Anthology ID:
D19-5633
Volume:
Proceedings of the 3rd Workshop on Neural Generation and Translation
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | NGT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
289–296
Language:
URL:
https://www.aclweb.org/anthology/D19-5633
DOI:
10.18653/v1/D19-5633
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5633.pdf