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Characterizing the Dynamics of Learning in Repeated Reference Games

机译：在重复参考游戏中表征学习的动态

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摘要

The language we use over the course of conversation changes as we establish common ground and learn what our partner finds meaningful. Here we draw upon recent advances in natural language processing to provide a finer-grained characterization of the dynamics of this learning process. We release an open corpus (15,000 utterances) of extended dyadic interactions in a classic repeated reference game task where pairs of participants had to coordinate on how to refer to initially difficult-to-describe tangram stimuli. We find that different pairs discover a wide variety of idiosyncratic but efficient and stable solutions to the problem of reference. Furthermore, these conventions are shaped by the communicative context: words that are more discriminative in the initial context (i.e., that are used for one target more than others) are more likely to persist through the final repetition. Finally, we find systematic structure in how a speaker's referring expressions become more efficient over time: Syntactic units drop out in clusters following positive feedback from the listener, eventually leaving short labels containing open-class parts of speech. These findings provide a higher resolution look at the quantitative dynamics of ad hoc convention formation and support further development of computational models of learning in communication.
机译：我们在对话过程中使用的语言发生了变化，因为我们建立了共同点，了解我们的伴侣发现有意义的东西。在这里，我们借鉴了自然语言处理的最近进步，以提供这种学习过程的动态的更精细粒度。我们在经典重复参考游戏任务中释放开放语料库（> 15,000个话语），在经典重复参考游戏任务中，参与者对如何协调如何参考最初难以描述的棕褐色刺激。我们发现不同的对发现各种特殊但有效且稳定的解决方案对参考问题。此外，这些公约由交际语境形状成形：在初始上下文中更具判别的单词（即，用于一个目标超过其他目标）更有可能持续到最后重复。最后，我们发现扬声器的引用表达式如何更有效地找到系统的结构：句法单元在侦听器的积极反馈之后的群集中删除，最终留下包含开放类别的短标签。这些调查结果提供了更高的分辨率看，临时会议形成的定量动态，并支持进一步发展沟通学习的计算模型。

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