Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game
This addresses the challenge of adaptive and efficient language acquisition for AI agents, though it appears incremental in combining imitation and reinforcement learning for a specific domain.
The paper tackles the problem of building intelligent agents that can learn language and acquire new visual concepts through conversational interaction, achieving one-shot learning of novel objects and using that knowledge in subsequent conversations.
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.