论文标题
对自动分析对话的神经模型的调查：更好地整合社会科学
A survey of neural models for the automatic analysis of conversation: Towards a better integration of the social sciences
论文作者
论文摘要
在过去的几年中，引入了一些令人兴奋的神经体系结构的新方法进行对话分析。这些包括用于检测情感，对话行为和情感极性的神经体系结构。他们利用了当代机器学习的一些关键属性，例如具有注意机制和基于变压器的方法的经常性神经网络。但是，尽管体系结构本身非常有前途，但迄今为止，它们已应用的现象只是使对话引人入胜的一小部分。在本文中，我们调查了这些神经体系结构及其所应用的内容。然后，在社会科学文献的基础上，我们将对话的最基本和定义性特征描述为对话的最基本和定义性特征，这是两个或多个对话者随着时间的推移的共同构建。我们讨论如何将所调查的类型的神经体系结构用于对话的这些更基本的方面，以及这在更好地分析对话的方面，甚至从长远来看，这是一种更好的方式来为对话系统生成对话。
Some exciting new approaches to neural architectures for the analysis of conversation have been introduced over the past couple of years. These include neural architectures for detecting emotion, dialogue acts, and sentiment polarity. They take advantage of some of the key attributes of contemporary machine learning, such as recurrent neural networks with attention mechanisms and transformer-based approaches. However, while the architectures themselves are extremely promising, the phenomena they have been applied to to date are but a small part of what makes conversation engaging. In this paper we survey these neural architectures and what they have been applied to. On the basis of the social science literature, we then describe what we believe to be the most fundamental and definitional feature of conversation, which is its co-construction over time by two or more interlocutors. We discuss how neural architectures of the sort surveyed could profitably be applied to these more fundamental aspects of conversation, and what this buys us in terms of a better analysis of conversation and even, in the longer term, a better way of generating conversation for a conversational system.
