Abstract
We consider the problem of learning to map from natural language instructions to state transitions (actions) in a data-efficient manner. Our method takes inspiration from the idea that it should be easier to ground language to concepts that have already been formed through pre-linguistic observation. We augment a baseline instruction-following learner with an initial environment-learning phase that uses observations of language-free state transitions to induce a suitable latent representation of actions before processing the instruction-following training data. We show that mapping to pre-learned representations substantially improves performance over systems whose representations are learned from limited instructional data alone.- Anthology ID:
- P19-1188
- 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:
- 1946–1956
- Language:
- URL:
- https://aclanthology.org/P19-1188
- DOI:
- 10.18653/v1/P19-1188
- Cite (ACL):
- David Gaddy and Dan Klein. 2019. Pre-Learning Environment Representations for Data-Efficient Neural Instruction Following. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1946–1956, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Pre-Learning Environment Representations for Data-Efficient Neural Instruction Following (Gaddy & Klein, ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/P19-1188.pdf
- Code
- dgaddy/environment-learning