Abstract
We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect. To that end, we first generalize syntactic linearization techniques to account for the richer semantic dependency graph structure. Following, we design a neural sequence-to-sequence framework which can effectively recover our graph linearizations, performing almost on-par with previous SDP state-of-the-art while requiring less parallel training annotations. Beyond SDP, our linearization technique opens the door to integration of graph-based semantic representations as features in neural models for downstream applications.- Anthology ID:
- D18-1263
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2412–2421
- Language:
- URL:
- https://aclanthology.org/D18-1263
- DOI:
- 10.18653/v1/D18-1263
- Cite (ACL):
- Gabriel Stanovsky and Ido Dagan. 2018. Semantics as a Foreign Language. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2412–2421, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Semantics as a Foreign Language (Stanovsky & Dagan, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/D18-1263.pdf
- Data
- Penn Treebank