@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://aclanthology.org/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.",
}
Markdown (Informal)
[Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction](https://aclanthology.org/W18-6902) (Belakova & Gkatzia, INLG 2018)
ACL