Attention Strategies for Multi-Source Sequence-to-Sequence Learning

Jindřich Libovický, Jindřich Helcl


Abstract
Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities. We propose two novel approaches to combine the outputs of attention mechanisms over each source sequence, flat and hierarchical. We compare the proposed methods with existing techniques and present results of systematic evaluation of those methods on the WMT16 Multimodal Translation and Automatic Post-editing tasks. We show that the proposed methods achieve competitive results on both tasks.
Anthology ID:
P17-2031
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
196–202
Language:
URL:
https://aclanthology.org/P17-2031
DOI:
10.18653/v1/P17-2031
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/P17-2031.pdf
Presentation:
 P17-2031.Presentation.pdf
Video:
 https://vimeo.com/234946385
Data
WMT 2016