SOCCER：一个体育评论领域的信息稀疏语篇状态跟踪集
SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain
摘要：
在追求自然语言理解的过程中，人们一直对跟踪整个叙述中的状态变化感兴趣。在建模以事务为中心的对话和过程文本的状态方面已经取得了令人印象深刻的进展。然而，这个问题已经不太深入研究的领域，一般话语的地面真理描述的状态可能是松散的定义和状态变化是不太密集分布在话语。本文提出转向简化的，完全可观察的系统，显示其中一些属性：体育赛事。我们策划了2，263场足球比赛，包括带有时间戳的自然语言评论，以及离散事件，例如球队进球，交换球员或被罚牌。我们提出了一个新的任务制定，在不同的时间戳的游戏评论的段落，系统被要求识别游戏中事件的发生。这个域允许对状态进行丰富的描述，同时避免了许多其他现实世界设置的复杂性。作为性能测量的初始点，我们包括两个基线方法从句子分类的角度与时间依赖和当前国家的最先进的生成模型，分别，并证明，即使是复杂的现有方法的斗争状态跟踪任务时，状态的定义扩大或非事件的喋喋不休变得普遍。
英文摘要：
In the pursuit of natural language understanding, there has been a long standing interest in tracking state changes throughout narratives. Impressive progress has been made in modeling the state of transaction-centric dialogues and procedural texts. However, this problem has been less intensively studied in the realm of general discourse where ground truth descriptions of states may be loosely defined and state changes are less densely distributed over utterances. This paper proposes to turn to simplified, fully observable systems that show some of these properties: Sports events. We curated 2,263 soccer matches including time-stamped natural language commentary accompanied by discrete events such as a team scoring goals, switching players or being penalized with cards. We propose a new task formulation where, given paragraphs of commentary of a game at different timestamps, the system is asked to recognize the occurrence of in-game events. This domain allows for rich descriptions of state while avoiding the complexities of many other real-world settings. As an initial point of performance measurement, we include two baseline methods from the perspectives of sentence classification with temporal dependence and current state-of-the-art generative model, respectively, and demonstrate that even sophisticated existing methods struggle on the state tracking task when the definition of state broadens or non-event chatter becomes prevalent.
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。
