Dynamic Meta-Embeddings for Improved Sentence Representations

Douwe Kiela, Changhan Wang, Kyunghyun Cho


Abstract
While one of the first steps in many NLP systems is selecting what pre-trained word embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves. To that end, we introduce dynamic meta-embeddings, a simple yet effective method for the supervised learning of embedding ensembles, which leads to state-of-the-art performance within the same model class on a variety of tasks. We subsequently show how the technique can be used to shed new light on the usage of word embeddings in NLP systems.
Anthology ID:
D18-1176
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1466–1477
Language:
URL:
https://www.aclweb.org/anthology/D18-1176
DOI:
10.18653/v1/D18-1176
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PDF:
http://aclanthology.lst.uni-saarland.de/D18-1176.pdf