Neural sentence generation from formal semantics
Kana Manome, Masashi Yoshikawa, Hitomi Yanaka, Pascual Martínez-Gómez, Koji Mineshima, Daisuke Bekki
Abstract
Sequence-to-sequence models have shown strong performance in a wide range of NLP tasks, yet their applications to sentence generation from logical representations are underdeveloped. In this paper, we present a sequence-to-sequence model for generating sentences from logical meaning representations based on event semantics. We use a semantic parsing system based on Combinatory Categorial Grammar (CCG) to obtain data annotated with logical formulas. We augment our sequence-to-sequence model with masking for predicates to constrain output sentences. We also propose a novel evaluation method for generation using Recognizing Textual Entailment (RTE). Combining parsing and generation, we test whether or not the output sentence entails the original text and vice versa. Experiments showed that our model outperformed a baseline with respect to both BLEU scores and accuracies in RTE.- Anthology ID:
- W18-6549
- Volume:
- Proceedings of the 11th International Conference on Natural Language Generation
- Month:
- November
- Year:
- 2018
- Address:
- Tilburg University, The Netherlands
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 408–414
- Language:
- URL:
- https://aclanthology.org/W18-6549
- DOI:
- 10.18653/v1/W18-6549
- Cite (ACL):
- Kana Manome, Masashi Yoshikawa, Hitomi Yanaka, Pascual Martínez-Gómez, Koji Mineshima, and Daisuke Bekki. 2018. Neural sentence generation from formal semantics. In Proceedings of the 11th International Conference on Natural Language Generation, pages 408–414, Tilburg University, The Netherlands. Association for Computational Linguistics.
- Cite (Informal):
- Neural sentence generation from formal semantics (Manome et al., INLG 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-6549.pdf