Abstract
Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost-aware sequence-to-sequence learning. In experiments on interactive neural machine translation, we find that the self-regulator discovers an 𝜖-greedy strategy for the optimal cost-quality trade-off by mixing different feedback types including corrections, error markups, and self-supervision. Furthermore, we demonstrate its robustness under domain shift and identify it as a promising alternative to active learning.- Anthology ID:
- P19-1029
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 303–315
- Language:
- URL:
- https://aclanthology.org/P19-1029
- DOI:
- 10.18653/v1/P19-1029
- Cite (ACL):
- Julia Kreutzer and Stefan Riezler. 2019. Self-Regulated Interactive Sequence-to-Sequence Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 303–315, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Self-Regulated Interactive Sequence-to-Sequence Learning (Kreutzer & Riezler, ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/P19-1029.pdf
- Code
- joeynmt/joeynmt + additional community code