Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism
Abstract
We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navigation. We follow the framework established by (Zang et al., 2018a) that proposes the use of a navigation graph as a knowledge base for the task. Our results show significant performance gains when translating instructions on previously unseen environments, therefore, improving the generalization capabilities of the model.- Anthology ID:
- 2020.winlp-1.24
- Volume:
- Proceedings of the Fourth Widening Natural Language Processing Workshop
- Month:
- July
- Year:
- 2020
- Address:
- Seattle, USA
- Editors:
- Rossana Cunha, Samira Shaikh, Erika Varis, Ryan Georgi, Alicia Tsai, Antonios Anastasopoulos, Khyathi Raghavi Chandu
- Venue:
- WiNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 96–98
- Language:
- URL:
- https://aclanthology.org/2020.winlp-1.24
- DOI:
- 10.18653/v1/2020.winlp-1.24
- Cite (ACL):
- Patricio Cerda-Mardini, Vladimir Araujo, and Álvaro Soto. 2020. Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism. In Proceedings of the Fourth Widening Natural Language Processing Workshop, pages 96–98, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism (Cerda-Mardini et al., WiNLP 2020)