Kutlak Roman, van Deemter Kees, Mellish Chris
Natural Language Generation Group, Computing Science Department, University of Aberdeen Aberdeen, UK.
Front Psychol. 2016 Aug 31;7:1275. doi: 10.3389/fpsyg.2016.01275. eCollection 2016.
This article presents a computational model of the production of referring expressions under uncertainty over the hearer's knowledge. Although situations where the hearer's knowledge is uncertain have seldom been addressed in the computational literature, they are common in ordinary communication, for example when a writer addresses an unknown audience, or when a speaker addresses a stranger. We propose a computational model composed of three complimentary heuristics based on, respectively, an estimation of the recipient's knowledge, an estimation of the extent to which a property is unexpected, and the question of what is the optimum number of properties in a given situation. The model was tested in an experiment with human readers, in which it was compared against the Incremental Algorithm and human-produced descriptions. The results suggest that the new model outperforms the Incremental Algorithm in terms of the proportion of correctly identified entities and in terms of the perceived quality of the generated descriptions.
本文提出了一种在听者知识不确定的情况下指代表达生成的计算模型。尽管听者知识不确定的情况在计算文献中很少被提及，但在日常交流中却很常见，例如当作者面向未知受众写作时，或者当说话者与陌生人交谈时。我们提出了一个由三种互补启发式方法组成的计算模型，分别基于对接收者知识的估计、对属性意外程度的估计以及给定情况下最佳属性数量的问题。该模型在一项针对人类读者的实验中进行了测试，在实验中它与增量算法和人类生成的描述进行了比较。结果表明，新模型在正确识别实体的比例和生成描述的感知质量方面优于增量算法。
