Abstract
RLIE-DQN is a recently proposed Reinforcement Learning-based Information Extraction (IE) technique which is able to incorporate external evidence during the extraction process. RLIE-DQN trains a single agent sequentially, training on one instance at a time. This results in significant training slowdown which is undesirable. We leverage recent advances in parallel RL training using asynchronous methods and propose RLIE-A3C. RLIE-A3C trains multiple agents in parallel and is able to achieve upto 6x training speedup over RLIE-DQN, while suffering no loss in average accuracy.- Anthology ID:
- D17-1281
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2658–2663
- Language:
- URL:
- https://aclanthology.org/D17-1281
- DOI:
- 10.18653/v1/D17-1281
- Cite (ACL):
- Aditya Sharma, Zarana Parekh, and Partha Talukdar. 2017. Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2658–2663, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods (Sharma et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/D17-1281.pdf
- Code
- adi-sharma/RLIE_A3C