# Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game

arXiv: https://arxiv.org/abs/1805.00462 · HTML: https://mlsanity.com/paper/1805.00462/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations](https://mlsanity.com/paper/2405.13828/) (2024)
- [Neural Abstructions: Abstractions that Support Construction for Grounded Language Learning](https://mlsanity.com/paper/2107.09285/) (2021)
- [Describe me an Aucklet: Generating Grounded Perceptual Category Descriptions](https://mlsanity.com/paper/2303.04053/) (2023)

## Abstract (arXiv)

Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training data, and is hardly adaptive to new scenarios or flexible for acquiring new knowledge without inefficient retraining or catastrophic forgetting. We highlight the perspective that conversational interaction serves as a natural interface both for language learning and for novel knowledge acquisition and propose a joint imitation and reinforcement approach for grounded language learning through an interactive conversational game. The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. Results compared with other methods verified the effectiveness of the proposed approach.