@inproceedings{gulati-agrawal-2017-playing,
title = "Playing with Embeddings : Evaluating embeddings for Robot Language Learning through {MUD} Games",
author = "Gulati, Anmol and
Agrawal, Kumar Krishna",
editor = "Bowman, Samuel and
Goldberg, Yoav and
Hill, Felix and
Lazaridou, Angeliki and
Levy, Omer and
Reichart, Roi and
S{\o}gaard, Anders",
booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5305",
doi = "10.18653/v1/W17-5305",
pages = "27--30",
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.",
}
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%0 Conference Proceedings
%T Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games
%A Gulati, Anmol
%A Agrawal, Kumar Krishna
%Y Bowman, Samuel
%Y Goldberg, Yoav
%Y Hill, Felix
%Y Lazaridou, Angeliki
%Y Levy, Omer
%Y Reichart, Roi
%Y Søgaard, Anders
%S Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F gulati-agrawal-2017-playing
%X 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.
%R 10.18653/v1/W17-5305
%U https://aclanthology.org/W17-5305
%U https://doi.org/10.18653/v1/W17-5305
%P 27-30
Markdown (Informal)
[Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games](https://aclanthology.org/W17-5305) (Gulati & Agrawal, RepEval 2017)
ACL