# Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings

arXiv: https://arxiv.org/abs/1709.10423 · HTML: https://mlsanity.com/paper/1709.10423/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Learning to generate and corr- uh I mean repair language in real-time](https://mlsanity.com/paper/2308.11683/) (2023)
- [Towards Learning Through Open-Domain Dialog](https://mlsanity.com/paper/2202.03040/) (2022)

## Abstract (arXiv)

We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users and achieve good learning performance (accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.