Abstract
Bandit structured prediction describes a stochastic optimization framework where learning is performed from partial feedback. This feedback is received in the form of a task loss evaluation to a predicted output structure, without having access to gold standard structures. We advance this framework by lifting linear bandit learning to neural sequence-to-sequence learning problems using attention-based recurrent neural networks. Furthermore, we show how to incorporate control variates into our learning algorithms for variance reduction and improved generalization. We present an evaluation on a neural machine translation task that shows improvements of up to 5.89 BLEU points for domain adaptation from simulated bandit feedback.- Anthology ID:
- P17-1138
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1503–1513
- Language:
- URL:
- https://aclanthology.org/P17-1138
- DOI:
- 10.18653/v1/P17-1138
- Cite (ACL):
- Julia Kreutzer, Artem Sokolov, and Stefan Riezler. 2017. Bandit Structured Prediction for Neural Sequence-to-Sequence Learning. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1503–1513, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Bandit Structured Prediction for Neural Sequence-to-Sequence Learning (Kreutzer et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P17-1138.pdf