Abstract
We propose a learning approach for mapping context-dependent sequential instructions to actions. We address the problem of discourse and state dependencies with an attention-based model that considers both the history of the interaction and the state of the world. To train from start and goal states without access to demonstrations, we propose SESTRA, a learning algorithm that takes advantage of single-step reward observations and immediate expected reward maximization. We evaluate on the SCONE domains, and show absolute accuracy improvements of 9.8%-25.3% across the domains over approaches that use high-level logical representations.- Anthology ID:
- P18-1193
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2072–2082
- Language:
- URL:
- https://aclanthology.org/P18-1193
- DOI:
- 10.18653/v1/P18-1193
- Cite (ACL):
- Alane Suhr and Yoav Artzi. 2018. Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2072–2082, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation (Suhr & Artzi, ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P18-1193.pdf
- Code
- clic-lab/scone
- Data
- ATIS