论文标题
DIFFG-RL：利用状态与常识之间的差异
DiffG-RL: Leveraging Difference between State and Common Sense
论文作者
论文摘要
考虑到背景知识，作为环境一直是解决涉及自然语言的任务的重要组成部分。此类任务的一个代表性示例是基于文本的游戏，玩家需要根据游戏先前显示的文本以及他们自己的背景知识对语言和常识进行决定。在这项工作中，我们不仅会研究具有常识，而且还可以看出，在先前的研究中可以看出，而且它的有效用途也是如此。我们假设环境的一部分与常识不同，应构成行动选择的理由之一。我们提出了一种新颖的代理，diffg-rl，该代理构建了一个差异图，该图通过使用专用图编码器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数量和表示图表的构造。我们在基于文本的游戏的实验中验证了DIFFG-RL，这些游戏需要常识，并表明它的表现优于基准的分数的17％。该代码可在https://github.com/ibm/diffg-rl上找到
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
