Abstract
We propose a graph-based method to tackle the dependency tree linearization task. We formulate the task as a Traveling Salesman Problem (TSP), and use a biaffine attention model to calculate the edge costs. We facilitate the decoding by solving the TSP for each subtree and combining the solution into a projective tree. We then design a transition system as post-processing, inspired by non-projective transition-based parsing, to obtain non-projective sentences. Our proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding.- Anthology ID:
- 2020.acl-main.134
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1451–1462
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.134
- DOI:
- 10.18653/v1/2020.acl-main.134
- Cite (ACL):
- Xiang Yu, Simon Tannert, Ngoc Thang Vu, and Jonas Kuhn. 2020. Fast and Accurate Non-Projective Dependency Tree Linearization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1451–1462, Online. Association for Computational Linguistics.
- Cite (Informal):
- Fast and Accurate Non-Projective Dependency Tree Linearization (Yu et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2020.acl-main.134.pdf