Efficient, Compositional, Order-sensitive n-gram Embeddings

Adam Poliak, Pushpendre Rastogi, M. Patrick Martin, Benjamin Van Durme


Abstract
We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive. Our method creates decompositional embeddings for words offline and combines them to create new embeddings for phrases in real time. Unlike other approaches, ECO can create embeddings for phrases not seen during training. We evaluate ECO on supervised and unsupervised tasks and demonstrate that creating phrase embeddings that are sensitive to word order can help downstream tasks.
Anthology ID:
E17-2081
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
503–508
Language:
URL:
https://www.aclweb.org/anthology/E17-2081
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-2081.pdf