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Invited Speaker: Christopher Potts: Learning in Extended and Approximate Rational Speech Acts Models

机译:邀请演讲者:克里斯托弗·帕特:在延长和近似合理演讲的运动中学习

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The Rational Speech Acts (RSA) model treats language use as a recursive process in which probabilistic speaker and listener agents reason about each other's intentions to enrich, and negotiate, the semantics of their language along broadly Gricean lines. RSA builds on early work by the philosopher David Lewis and others on signaling systems as well as more recent developments in Bayesian cognitive modeling. Over the last five years, RSA has been shown to provide a unified account of numerous core phenomena in pragmatics, including metaphor, hyperbole, sarcasm, politeness, and a wide range of conversational implicatures. Its precise, quantitative nature has also facilitated an outpouring of new experimental work on these phenomena. However, applications of RSA to large-scale problems in NLP and AI have so far been limited, because the exact version of the model is intractable along several dimensions. In this talk, I'll report on recent progress in approximating RSA in ways that retains its core properties while enabling application to large datasets and complex environments in which language and action are brought together.
机译:理性语音行为(RSA)模型将语言用作作为递归过程,其中概率扬声器和听众代理的原因是彼此的意图,以丰富,伴随着广泛的Gricean线路的语言。 RSA在哲学家大卫刘易斯和其他信令系统上的其他工作中建立了早期工作,以及贝叶斯认知建模的更新发展。在过去的五年中,RSA已被证明可以在语用学中提供众多核心现象的统一账户,包括隐喻,夸张,讽刺,礼貌和广泛的会话意识。其精确的定量性质还促进了对这些现象的新实验工作的推出。然而,RSA在NLP和AI中的大规模问题的应用到目前为止已受到限制,因为模型的确切版本沿几个维度难以解决。在此谈话中,我将以近似RSA的方式报告最近的进展,以保留其核心属性,同时使应用于大型数据集和语言和动作的复杂环境。

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