A Paradigm for Situated and Goal-Driven Language Learning
This work addresses the challenge of building AI agents that can effectively integrate into human contexts for natural language dialogue, though it appears incremental as it proposes a general paradigm without specific breakthroughs.
The paper tackles the problem of developing communicative agents that can flexibly use language in diverse contexts, proposing a situated language learning paradigm aimed at creating robust agents for productive human collaboration.
A distinguishing property of human intelligence is the ability to flexibly use language in order to communicate complex ideas with other humans in a variety of contexts. Research in natural language dialogue should focus on designing communicative agents which can integrate themselves into these contexts and productively collaborate with humans. In this abstract, we propose a general situated language learning paradigm which is designed to bring about robust language agents able to cooperate productively with humans.