Abstract
Acquiring language provides a ubiquitous mode of communication, across humans and robots. To this effect, distributional representations of words based on co-occurrence statistics, have provided significant advancements ranging across machine translation to comprehension. In this paper, we study the suitability of using general purpose word-embeddings for language learning in robots. We propose using text-based games as a proxy to evaluating word embedding on real robots. Based in a risk-reward setting, we review the effectiveness of the embeddings in navigating tasks in fantasy games, as an approximation to their performance on more complex scenarios, like language assisted robot navigation.- Anthology ID:
- W17-5305
- Volume:
- Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Samuel Bowman, Yoav Goldberg, Felix Hill, Angeliki Lazaridou, Omer Levy, Roi Reichart, Anders Søgaard
- Venue:
- RepEval
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 27–30
- Language:
- URL:
- https://aclanthology.org/W17-5305
- DOI:
- 10.18653/v1/W17-5305
- Cite (ACL):
- Anmol Gulati and Kumar Krishna Agrawal. 2017. Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games. In Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP, pages 27–30, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games (Gulati & Agrawal, RepEval 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-5305.pdf