Combining unsupervised and supervised grounding approaches
Par Oliver Roesler
There exist a variety of grounding approaches that either utilize supervised or unsupervised learning techniques to ground words through corresponding percepts. Supervised approaches are usually sample efficient but depend on the availability and trustworthiness of a tutor, while unsupervised approaches avoid this dependency, yet, they are less sample efficient and often also less accurate. So far, only limited work has been done to combine both approaches. In this talk, I will present recent work on combining cross-situational learning and interactive learning approaches to enable artificial language learners to benefit from the support and feedback of another agent, e.g., a human, without depending on it.
Roesler, O. (2020). Unsupervised Online Grounding of Natural Language during Human-Robot Interactions. arXiv preprint arXiv:2007.04304.
Roesler, O. (2020). Enhancing Unsupervised Natural Language Grounding through Explicit Teaching. In Proceedings of The 3rd UK-RAS Conference.
Oliver Roesler, Brain Embodiment Lab, University of Reading; AI Lab, Vrije Universiteit Brussel; and Modality.AI, Inc. focuses on the development of mechanisms to enable natural, adaptive, and open-ended human-agent interactions through an interdisciplinary approach that combines work in knowledge representation and reasoning, language grounding, reinforcement learning based action learning as well as multimodal prediction of human affective and mental states.
Le lien zoom pour y assister est (veuillez indiquer votre nom complet dès votre entrée pour nous faciliter la tâche de vous admettre au séminaire) : https://uqam.zoom.us/j/84473395235