Sequence-to-sequence Models for Cache Transition Systems

Xiaochang Peng, Linfeng Song, Daniel Gildea, Giorgio Satta


Abstract
In this paper, we present a sequence-to-sequence based approach for mapping natural language sentences to AMR semantic graphs. We transform the sequence to graph mapping problem to a word sequence to transition action sequence problem using a special transition system called a cache transition system. To address the sparsity issue of neural AMR parsing, we feed feature embeddings from the transition state to provide relevant local information for each decoder state. We present a monotonic hard attention model for the transition framework to handle the strictly left-to-right alignment between each transition state and the current buffer input focus. We evaluate our neural transition model on the AMR parsing task, and our parser outperforms other sequence-to-sequence approaches and achieves competitive results in comparison with the best-performing models.
Anthology ID:
P18-1171
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1842–1852
Language:
URL:
https://aclanthology.org/P18-1171
DOI:
10.18653/v1/P18-1171
Bibkey:
Cite (ACL):
Xiaochang Peng, Linfeng Song, Daniel Gildea, and Giorgio Satta. 2018. Sequence-to-sequence Models for Cache Transition Systems. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1842–1852, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Sequence-to-sequence Models for Cache Transition Systems (Peng et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1171.pdf
Presentation:
 P18-1171.Presentation.pdf
Video:
 https://vimeo.com/285804815
Code
 xiaochang13/CacheTransition-Seq2seq