Taking into account background knowledge as the context has always been an important part of solving tasks that involve natural language. One representative example of such tasks is text-based games, where players need to make decisions based on both description text previously shown in the game, and their own background knowledge about the language and common sense. In this work, we investigate not simply giving common sense, as can be seen in prior research, but also its effective usage. We assume that a part of the environment states different from common sense should constitute one of the grounds for action selection. We propose a novel agent, DiffG-RL, which constructs a Difference Graph that organizes the environment states and common sense by means of interactive objects with a dedicated graph encoder. DiffG-RL also contains a framework for extracting the appropriate amount and representation of common sense from the source to support the construction of the graph. We validate DiffG-RL in experiments with text-based games that require common sense and show that it outperforms baselines by 17% of scores. The code is available at https://github.com/ibm/diffg-rl
翻译:考虑到背景知识,环境背景始终是解决涉及自然语言的任务的一个重要部分。这种任务的一个有代表性的例子是基于文本的游戏,游戏参与者需要根据游戏中先前显示的描述文字以及他们自己对语言和常识的背景知识作出决定。在这项工作中,我们调查的不仅仅是提供常识,从先前的研究中可以看到这一点,而且还有其有效的使用。我们假定,环境中与常识不同的部分国家应该构成选择行动的理由之一。我们提议了一个新颖的代理机构DiffG-RL,它构建一个差异图,用专门的图形编码来组织环境状态和常识。DiffG-RL还包含一个框架,用于从源中提取适当的常识量和表示以支持图的构造。我们用基于文本的游戏试验来验证DiffG-RL,这些游戏需要常识,并显示它超过分数的17%的基线。该代码可在https://github.com/ibm/diffg-rl上查阅。