An Empirical Study of Building a Strong Baseline for Constituency Parsing
Jun Suzuki, Sho Takase, Hidetaka Kamigaito, Makoto Morishita, Masaaki Nagata
Abstract
This paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. We incorporate several techniques that were mainly developed in natural language generation tasks, e.g., machine translation and summarization, and demonstrate that the sequence-to-sequence model achieves the current top-notch parsers’ performance (almost) without requiring any explicit task-specific knowledge or architecture of constituent parsing.- Anthology ID:
- P18-2097
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 612–618
- Language:
- URL:
- https://aclanthology.org/P18-2097
- DOI:
- 10.18653/v1/P18-2097
- Cite (ACL):
- Jun Suzuki, Sho Takase, Hidetaka Kamigaito, Makoto Morishita, and Masaaki Nagata. 2018. An Empirical Study of Building a Strong Baseline for Constituency Parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 612–618, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- An Empirical Study of Building a Strong Baseline for Constituency Parsing (Suzuki et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/P18-2097.pdf
- Code
- nttcslab-nlp/strong_s2s_baseline_parser
- Data
- Penn Treebank