Abstract
The task of linearization is to find a grammatical order given a set of words. Traditional models use statistical methods. Syntactic linearization systems, which generate a sentence along with its syntactic tree, have shown state-of-the-art performance. Recent work shows that a multilayer LSTM language model outperforms competitive statistical syntactic linearization systems without using syntax. In this paper, we study neural syntactic linearization, building a transition-based syntactic linearizer leveraging a feed forward neural network, observing significantly better results compared to LSTM language models on this task.- Anthology ID:
- W18-6553
- Volume:
- Proceedings of the 11th International Conference on Natural Language Generation
- Month:
- November
- Year:
- 2018
- Address:
- Tilburg University, The Netherlands
- Editors:
- Emiel Krahmer, Albert Gatt, Martijn Goudbeek
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 431–440
- Language:
- URL:
- https://aclanthology.org/W18-6553
- DOI:
- 10.18653/v1/W18-6553
- Cite (ACL):
- Linfeng Song, Yue Zhang, and Daniel Gildea. 2018. Neural Transition-based Syntactic Linearization. In Proceedings of the 11th International Conference on Natural Language Generation, pages 431–440, Tilburg University, The Netherlands. Association for Computational Linguistics.
- Cite (Informal):
- Neural Transition-based Syntactic Linearization (Song et al., INLG 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W18-6553.pdf