@inproceedings{belakova-gkatzia-2018-learning,
    title = "Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction",
    author = "Belakova, Jekaterina  and
      Gkatzia, Dimitra",
    editor = "Foster, Mary Ellen  and
      Buschmeier, Hendrik  and
      Gkatzia, Dimitra",
    booktitle = "Proceedings of the Workshop on {NLG} for Human{--}Robot Interaction",
    month = nov,
    year = "2018",
    address = "Tilburg, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-6902/",
    doi = "10.18653/v1/W18-6902",
    pages = "8--11",
    abstract = "One of the most natural ways for human robot communication is through spoken language. Training human-robot interaction systems require access to large datasets which are expensive to obtain and labour intensive. In this paper, we describe an approach for learning from minimal data, using as a toy example language understanding in spoken dialogue systems. Understanding of spoken language is crucial because it has implications for natural language generation, i.e. correctly understanding a user{'}s utterance will lead to choosing the right response/action. Finally, we discuss implications for Natural Language Generation in Human-Robot Interaction."
}