论文标题
学会通过摊销务实的推理来参考信息
Learning to refer informatively by amortizing pragmatic reasoning
论文作者
论文摘要
人类语言的标志是能够有效，有效地传达上下文相关信息的能力。关于人类原因如何在《理性言论法案》（RSA）框架中提出的一种理论，该框架通过递归社会推理的过程捕获了实用现象（Goodman＆Frank，2016年）。但是，RSA在不受约束的环境中代表理想的推理。我们探讨了这样一个想法，即说话者可能会通过直接优化与内部听众模型的成功沟通来学会随着时间的推移摊销RSA计算的成本。在代表综合和人类生成数据的两个通信游戏数据集中与接地的神经扬声器和听众的模拟中，我们发现我们的摊销模型能够快速生成在一系列环境中有效和简洁的语言，而无需明确的务实推理。
A hallmark of human language is the ability to effectively and efficiently convey contextually relevant information. One theory for how humans reason about language is presented in the Rational Speech Acts (RSA) framework, which captures pragmatic phenomena via a process of recursive social reasoning (Goodman & Frank, 2016). However, RSA represents ideal reasoning in an unconstrained setting. We explore the idea that speakers might learn to amortize the cost of RSA computation over time by directly optimizing for successful communication with an internal listener model. In simulations with grounded neural speakers and listeners across two communication game datasets representing synthetic and human-generated data, we find that our amortized model is able to quickly generate language that is effective and concise across a range of contexts, without the need for explicit pragmatic reasoning.
