Abstract
Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations. We propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework. Our encoder is a discriminative neural semantic dependency parser that predicts the latent parse graph of the input sentence. Our decoder is a generative neural model that reconstructs the input sentence conditioned on the latent parse graph. Our model is arc-factored and therefore parsing and learning are both tractable. Experiments show our model achieves significant and consistent improvement over the supervised baseline.- Anthology ID:
- 2020.acl-main.607
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6795–6805
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.607
- DOI:
- 10.18653/v1/2020.acl-main.607
- Cite (ACL):
- Zixia Jia, Youmi Ma, Jiong Cai, and Kewei Tu. 2020. Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6795–6805, Online. Association for Computational Linguistics.
- Cite (Informal):
- Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders (Jia et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2020.acl-main.607.pdf