是的，这边走！在支持性教师的情节内反馈下学习将指代表达式锚定为动作
Yes, this Way! Learning to Ground Referring Expressions into Actions with Intra-episodic Feedback from Supportive Teachers
AI总结：
本文研究协作交互中的情节内反馈，将指称语言游戏建模为稀疏奖励强化学习问题，学习跟随者策略，发现情节内反馈能提升模型在场景复杂度上的泛化能力，优于仅提供初始指令。
摘要：
在持续交互中捕捉语言信号的能力，对于未来的机器学习模型与人类自然协作和交互至关重要。本文提出了一项初步研究，评估在协作环境中给出的情节内反馈。我们使用指称语言游戏作为面向任务的协作联合活动的可控示例。教师发出由著名的符号算法（即“Incremental Algorithm”）生成的指代表达式作为初始指令，然后监控跟随者的动作，以可能通过情节内反馈进行干预（无需明确请求该反馈）。我们将此任务构建为具有稀疏奖励的强化学习问题，并为启发式教师学习跟随者策略。结果表明，情节内反馈使跟随者能够在场景复杂度的各个方面进行泛化，并且表现优于仅提供初始陈述。
英文摘要：
The ability to pick up on language signals in an ongoing interaction is crucial for future machine learning models to collaborate and interact with humans naturally. In this paper, we present an initial study that evaluates intra-episodic feedback given in a collaborative setting. We use a referential language game as a controllable example of a task-oriented collaborative joint activity. A teacher utters a referring expression generated by a well-known symbolic algorithm (the "Incremental Algorithm") as an initial instruction and then monitors the follower's actions to possibly intervene with intra-episodic feedback (which does not explicitly have to be requested). We frame this task as a reinforcement learning problem with sparse rewards and learn a follower policy for a heuristic teacher. Our results show that intra-episodic feedback allows the follower to generalize on aspects of scene complexity and performs better than providing only the initial statement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
