用概率模型模拟对话隐含意义的推理机制
Conversational Implicatures: Modelling Relevance Theory Probabilistically
- 基于贝叶斯框架建模语用推理，将隐含意义转化为概率计算
- 通过对话隐含意义案例验证模型可捕捉人类推理规律
- 适合对语用学与认知建模感兴趣的学者参考
近年来，贝叶斯概率理论在认知科学中的应用，结合新一代概率计算工具，推动了语用学与语义学的‘概率转向’。特别是理性言语行为理论（Rational Speech Act theory）已成功以贝叶斯方式建模格里高意式语用现象，从简单的指称游戏扩展到复杂的语言推理。本文探讨如何将类似贝叶斯方法应用于关联理论（Sperber & Wilson, 1995）语用学，聚焦于对话隐含意义（conversational implicatures）这一典型语用现象，尝试建立概率化推理框架。
原文摘要 · Abstract (English)
Recent advances in Bayesian probability theory and its application to cognitive science in combination with the development of a new generation of computational tools and methods for probabilistic computation have led to a 'probabilistic turn' in pragmatics and semantics. In particular, the framework of Rational Speech Act theory has been developed to model broadly Gricean accounts of pragmatic phenomena in Bayesian terms, starting with fairly simple reference games and covering ever more complex communicative exchanges such as verbal syllogistic reasoning. This paper explores in which way a similar Bayesian approach might be applied to relevance-theoretic pragmatics (Sperber & Wilson, 1995) by study a paradigmatic pragmatic phenomenon: the communication of implicit meaning by ways of (conversational) implicatures.
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