@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://preview.aclanthology.org/iwcs-25-ingestion/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."
}