Better Surface Realization through Psycholinguistics
Better Surface Realization through Psycholinguistics
复制标题
通过心理语言学更好的表面认识
DOI：
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发表时间：
2014
影响因子：
2.5
通讯作者：
Michael White
中科院分区：
文献类型：
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作者：
Rajakrishnan Rajkumar;Michael White
在这项调查中，我们回顾了自然语言生成（NLG）中表面实现的最新进展，强调了机器学习模型如何超越n-gram，成功地将语言学见解融入日益丰富的模型中。我们还提出了这样一个观点，即通过从心理语言学的研究中获得深刻的见解，NLG仍然有很大的收获-不仅是人类的生产，而且是理解。我们强调如何实现排名模型可以通过模拟记忆在人类语言理解中的作用来改善，并讨论表面实现者如何过渡到使用为计算心理语言学中的增量解析开发的语法，从而使它们更适合集成到实时增量对话系统中。从生产的角度来看，我们认为，均匀的信息密度的原则有可能提高NLG的选择决策的理论基础，并讨论在这个方向上的两个初步步骤。最后，我们结束了我们的调查与讨论的前景，以社区为基础的评价表面实现系统。
In this survey, we review recent progress on surface realization in natural language generation (NLG), highlighting how machine learning models have moved beyond n-grams to successfully incorporate linguistic insights into increasingly rich models. We also advance the view that NLG still has much to gain by taking up insights from psycholinguistic studies – not only of human production but also of comprehension. We highlight how realization ranking models can be improved by modeling the role of memory in human language comprehension and discuss how surface realizers might transition to using grammars developed for incremental parsing in computational psycholinguistics, thereby making them more suitable for integration into real-time incremental dialog systems. From a production standpoint, we suggest that the principle of uniform information density has the potential to enhance the theoretical basis for choice making in NLG and discuss two initial steps in this direction. Finally, we conclude our survey with a discussion of prospects for community-based evaluation of surface realization systems.
使用心理语言驱动的树邻接语法进行增量预测解析
影响因子：
9.3
作者：
V. Demberg;F. Keller;A. Koller
通讯作者：
A. Koller