摘要翻译：
本文提出了一种学习将描述动态系统的简单叙述（即文本（句子序列）翻译成连贯的事件序列的方法，而不需要标记训练数据。我们的方法以事件的前提条件和影响的形式结合了领域知识，我们表明它在从RoboCup足球比赛的解说中重建RoboCup足球比赛的任务上优于最先进的监督学习系统。
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英文标题：
《Reasoning about RoboCup Soccer Narratives》
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作者：
Hannaneh Hajishirzi, Julia Hockenmaier, Erik T. Mueller, Eyal Amir
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper presents an approach for learning to translate simple narratives, i.e., texts (sequences of sentences) describing dynamic systems, into coherent sequences of events without the need for labeled training data. Our approach incorporates domain knowledge in the form of preconditions and effects of events, and we show that it outperforms state-of-the-art supervised learning systems on the task of reconstructing RoboCup soccer games from their commentaries.
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PDF链接：
https://arxiv.org/pdf/1202.3728