Abstract
We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach. While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires inflecting named entities, delexicalization or copy mechanisms do not work out-of-the-box and lexicalizing the generated outputs is non-trivial. In our experiments, we present two different approaches to this this problem: (1) using a neural language model to select the correct inflected form while lexicalizing, (2) a two-step generation setup: our sequence-to-sequence model generates an interleaved sequence of lemmas and morphological tags, which are then inflected by a morphological generator.- Anthology ID:
- W19-8670
- Volume:
- Proceedings of the 12th International Conference on Natural Language Generation
- Month:
- October–November
- Year:
- 2019
- Address:
- Tokyo, Japan
- Editors:
- Kees van Deemter, Chenghua Lin, Hiroya Takamura
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 563–574
- Language:
- URL:
- https://aclanthology.org/W19-8670
- DOI:
- 10.18653/v1/W19-8670
- Cite (ACL):
- Ondřej Dušek and Filip Jurčíček. 2019. Neural Generation for Czech: Data and Baselines. In Proceedings of the 12th International Conference on Natural Language Generation, pages 563–574, Tokyo, Japan. Association for Computational Linguistics.
- Cite (Informal):
- Neural Generation for Czech: Data and Baselines (Dušek & Jurčíček, INLG 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W19-8670.pdf