论文标题
提供基于文本的模拟环境的外部知识数据库的负担提取
Affordance Extraction with an External Knowledge Database for Text-Based Simulated Environments
论文作者
论文摘要
事实证明，基于文本的模拟环境是机器学习方法的有效测试床。可负担提取的过程可用于在这种环境中生成可能的互动操作。在本文中，研究了在负担得出过程中利用外部知识数据库（特别是概念网）的功能和挑战。在交互式小说（IF）平台Textworld和Jericho上引入和评估了一种用于自动负担提取的算法。为此，将收集的负担转换为IF代理的文本命令。为了探测自动评估过程的质量，进行了另外的人类基线研究。本文说明，尽管有一些挑战，但外部数据库原则上可以用于提取。本文以建议进一步修改和改进过程的建议结束。
Text-based simulated environments have proven to be a valid testbed for machine learning approaches. The process of affordance extraction can be used to generate possible actions for interaction within such an environment. In this paper the capabilities and challenges for utilizing external knowledge databases (in particular ConceptNet) in the process of affordance extraction are studied. An algorithm for automated affordance extraction is introduced and evaluated on the Interactive Fiction (IF) platforms TextWorld and Jericho. For this purpose, the collected affordances are translated into text commands for IF agents. To probe the quality of the automated evaluation process, an additional human baseline study is conducted. The paper illustrates that, despite some challenges, external databases can in principle be used for affordance extraction. The paper concludes with recommendations for further modification and improvement of the process.
