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
- Venue:
- NGT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 289–296
- Language:
- URL:
- https://aclanthology.org/D19-5633
- DOI:
- 10.18653/v1/D19-5633
- Cite (ACL):
- Lesly Miculicich, Marc Marone, and Hany Hassan. 2019. Selecting, Planning, and Rewriting: A Modular Approach for Data-to-Document Generation and Translation. In Proceedings of the 3rd Workshop on Neural Generation and Translation, pages 289–296, Hong Kong. Association for Computational Linguistics.
- Cite (Informal):
- Selecting, Planning, and Rewriting: A Modular Approach for Data-to-Document Generation and Translation (Miculicich et al., NGT 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D19-5633.pdf