Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction

Research Output

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.

Date:

31 December 2018
Publication Status:

Published
Funders:

Edinburgh Napier Funded

http://researchrepository.napier.ac.uk/output/1791784 <p>Belakova, J., & Gkatzia, D. (2018). Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction. In <i>Proceedings of the Workshop on NLG for Human–Robot Interaction</i>. , (8-11)</p>

Citation

Belakova, J., & Gkatzia, D. (2018). Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction. In Proceedings of the Workshop on NLG for Human–Robot Interaction. , (8-11)