All Outputs (3)

Optimising strategies for learning visually grounded word meanings through interaction (2018)
Thesis
Yu, Y. (2018). Optimising strategies for learning visually grounded word meanings through interaction. (Thesis)

Language Grounding is a fundamental problem in AI, regarding how symbols in Natural Language (e.g. words and phrases) refer to aspects of the physical environment (e.g. ob jects and attributes). In this thesis, our ultimate goal is to address an inte... Read More about Optimising strategies for learning visually grounded word meanings through interaction.

Incrementally learning semantic attributes through dialogue interaction (2018)
Presentation / Conference Contribution
Vanzo, A., Part, J. L., Yu, Y., Nardi, D., & Lemon, O. (2018). Incrementally learning semantic attributes through dialogue interaction. In AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (865-873)

Enabling a robot to properly interact with users plays a key role in the effective deployment of robotic platforms in domestic environments. Robots must be able to rely on interaction to improve their behaviour and adaptively understand their operati... Read More about Incrementally learning semantic attributes through dialogue interaction.

An Incremental Dialogue System for Learning Visually Grounded Word Meanings (demonstration system) (2018)
Presentation / Conference Contribution
Yu, Y., Eshghi, A., & Lemon, O. (2018, June). An Incremental Dialogue System for Learning Visually Grounded Word Meanings (demonstration system). Poster presented at Workshop on Dialogue and Perception 2018, Gothenburg