Character-level Supervision for Low-resource POS Tagging

Katharina Kann, Johannes Bjerva, Isabelle Augenstein, Barbara Plank, Anders Søgaard


Abstract
Neural part-of-speech (POS) taggers are known to not perform well with little training data. As a step towards overcoming this problem, we present an architecture for learning more robust neural POS taggers by jointly training a hierarchical, recurrent model and a recurrent character-based sequence-to-sequence network supervised using an auxiliary objective. This way, we introduce stronger character-level supervision into the model, which enables better generalization to unseen words and provides regularization, making our encoding less prone to overfitting. We experiment with three auxiliary tasks: lemmatization, character-based word autoencoding, and character-based random string autoencoding. Experiments with minimal amounts of labeled data on 34 languages show that our new architecture outperforms a single-task baseline and, surprisingly, that, on average, raw text autoencoding can be as beneficial for low-resource POS tagging as using lemma information. Our neural POS tagger closes the gap to a state-of-the-art POS tagger (MarMoT) for low-resource scenarios by 43%, even outperforming it on languages with templatic morphology, e.g., Arabic, Hebrew, and Turkish, by some margin.
Anthology ID:
W18-3401
Volume:
Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP
Month:
July
Year:
2018
Address:
Melbourne
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–11
Language:
URL:
https://aclanthology.org/W18-3401
DOI:
10.18653/v1/W18-3401
Bibkey:
Cite (ACL):
Katharina Kann, Johannes Bjerva, Isabelle Augenstein, Barbara Plank, and Anders Søgaard. 2018. Character-level Supervision for Low-resource POS Tagging. In Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP, pages 1–11, Melbourne. Association for Computational Linguistics.
Cite (Informal):
Character-level Supervision for Low-resource POS Tagging (Kann et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/W18-3401.pdf