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.- Anthology ID:
- W18-6902
- Volume:
- Proceedings of the Workshop on NLG for Human–Robot Interaction
- Month:
- November
- Year:
- 2018
- Address:
- Tilburg, The Netherlands
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8–11
- Language:
- URL:
- https://aclanthology.org/W18-6902
- DOI:
- 10.18653/v1/W18-6902
- Cite (ACL):
- Jekaterina Belakova and Dimitra Gkatzia. 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, pages 8–11, Tilburg, The Netherlands. Association for Computational Linguistics.
- Cite (Informal):
- Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction (Belakova & Gkatzia, INLG 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-6902.pdf