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
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 293–302
- Language:
- URL:
- https://aclanthology.org/N18-1027
- DOI:
- 10.18653/v1/N18-1027
- Cite (ACL):
- Marek Rei and Anders Søgaard. 2018. Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 293–302, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens (Rei & Søgaard, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/N18-1027.pdf
- Data
- FCE