Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens

Marek Rei, Anders Søgaard


Abstract
Can attention- or gradient-based visualization techniques be used to infer token-level labels for binary sequence tagging problems, using networks trained only on sentence-level labels? We construct a neural network architecture based on soft attention, train it as a binary sentence classifier and evaluate against token-level annotation on four different datasets. Inferring token labels from a network provides a method for quantitatively evaluating what the model is learning, along with generating useful feedback in assistance systems. Our results indicate that attention-based methods are able to predict token-level labels more accurately, compared to gradient-based methods, sometimes even rivaling the supervised oracle network.
Anthology ID:
N18-1027
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
293–302
Language:
URL:
https://www.aclweb.org/anthology/N18-1027
DOI:
10.18653/v1/N18-1027
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1027.pdf
Video:
 http://vimeo.com/276393910