Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models
Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models
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DOI：
10.1017/s1351324907004664
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发表时间：
2008-10
影响因子：
2.5
通讯作者：
A. Belz
中科院分区：
文献类型：
--
作者：
A. Belz
摘要 自然语言生成的两个重要趋势是（i）概率技术和（ii）摆脱传统严格模块化和顺序模型的综合方法。本文报告了实验，其中 pcru（一种将概率生成方法与生成空间综合模型相结合的生成框架）用于半自动创建五个不同版本的天气预报生成器。生成器在输出质量、开发时间和计算效率方面进行了评估，针对 (i) 人类预测者、(ii) 传统手工制作的管道 NLG 系统和 (iii) 卤素式统计生成器。最引人注目的结果是，尽管自动获得所有决策能力，但最好的 pcru 生成器产生的输出质量足够高，人类法官的评分比专家编写的预测更高。
Abstract Two important recent trends in natural language generation are (i) probabilistic techniques and (ii) comprehensive approaches that move away from traditional strictly modular and sequential models. This paper reports experiments in which pcru – a generation framework that combines probabilistic generation methodology with a comprehensive model of the generation space – was used to semi-automatically create five different versions of a weather forecast generator. The generators were evaluated in terms of output quality, development time and computational efficiency against (i) human forecasters, (ii) a traditional handcrafted pipelined nlg system and (iii) a halogen-style statistical generator. The most striking result is that despite acquiring all decision-making abilities automatically, the best pcru generators produce outputs of high enough quality to be scored more highly by human judges than forecasts written by experts.