Advice-Driven Learning: A Blocks-World and Fake News Detection Approach

Over the last few years, there has been growing interest in learning models for various Natural Language Processing tasks, such as the popular blocks world domain and fake news detection. These works typically view these problems as single-step processes, in which a human operator gives an instruction and an automated agent is evaluated on its ability to execute it. In this work, we take the first step towards increasing the bandwidth of this interaction, and suggest a protocol for including advice, high-level observations about the task, which can help constrain the agents prediction. Advice is designed to be a short natural language sentence, provided by the human operator in addition the original input for the task, that can help the agent improve its performance. We evaluate our advice-based approach on the blocks world task and fake-news detection, and show that even simple advice can help lead to significant performance improvements. To help reduce the effort involved in supplying the advice, we also explore model self-generated advice which can still improve results.