Argument Mining

争论和辩论构成了文明社会和智力生活的基石。论证过程运行着我们的政府，构建了科学努力，构建了宗教信仰。认识和理解论点是各行各业决策和专业活动的核心，这就是为什么我们把它们放在学术教育学和实践的中心；这就是为什么这些技能受到如此重视；也是为什么理性是人类的定义之一。我们关于论证结构的理论可以追溯到古希腊。在过去的大约30年里，计算科学已经开始基于这些理论来构建模型和设计软件：这是论点技术领域，最近活动的激增证明了该领域的活力和广泛的适用性。尽管以英国为世界领先者的辩论技术在医疗保健、公共政策、政府和媒体等领域都有应用，但人们的重点一直是人工智能技术，以支持人类辩论和随后的自动推理结果。在这类软件围墙花园之外提出的论点之所以不在议事日程上，只是因为机器对不受约束的自然发生推理的自动理解太难处理了。在2014年前，这项任务--论点挖掘--仅由多伦多、鲁汶和邓迪等极少数组织在特定领域进行投机性处理。截至2014年底，美国和欧盟的20多个研究实验室正在为解决这一问题做准备，举行了几次国际会议，包括在最大的计算语言学会议上举行的定期研讨会系列，并报告了数十项成果。这种活动大幅上升的原因在于成熟的技术和可用的回报。观点挖掘改变了市场研究和公关的进行方式，利用大数据分析技术来了解人们对产品和品牌的态度。情绪分析通过分析媒体表达的广泛情绪和观点，在预测金融市场方面产生了更大的影响。争论挖掘是这些技术的自然演变，提供了可用的详细程度的一步变化--不仅分析人们持有什么观点，而且分析他们为什么持有自己的观点。这就是为什么像IBM这样的主要组织对这项技术如此感兴趣的原因，我们与IBM在这个项目中进行了合作。论据技术中心现在管理着世界上最大的可公开访问的分析论据语料库，并拥有一个众所周知的、广泛使用的工具栈，用于管理数据集、进行分析和将结果可视化。这提供了一个独特的平台，我们既可以从这个平台上扩展现有的论点挖掘技术，也可以更雄心勃勃地使用论证哲学和修辞学的见解来转变论点挖掘技术的可靠性和适用性。特别是，我们将使用以刻板印象的推理模式为特征的论证方案理论来指导搜索论元成分的过程，并将修辞和比喻理论作为开发一类新的论元识别算法的基础。因此，我们将用详细的结构理论来转变纯统计驱动的方法，这些结构理论可以用一种限制机器学习任务的方式来定义预期，从而提高准确性和适用性。通过与国际商用机器公司和L科技公司(一家特定领域的中小企业)合作，该项目的目标不仅是从根本上提高这些技术的性能，确立英国的尖端地位，而且还将这些性能收益提供给最终用户。

Argument and debate form cornerstones of civilised society and of intellectual life. Processes of argumentation run our governments, structure scientific endeavour and frame religious belief. Recognising and understanding argument are central to decision-making and professional activity in all walks of life, which is why we place them at the centre of academic pedagogy and practice; it's why such a premium is placed upon these skills; and it's why rationality is one of the very defining notions of what it is to be human. Our theories of how argument is structured go back to Ancient Greece. In the past thirty years or so, the computational sciences have started to build models and engineer software based on these theories: this is the field of argument technology, and the recent surge in activity is testament to the vitality and broad applicability of the field. Though argument technology, in which the UK is a world leader, has had applications in domains as diverse as healthcare, public policy, government and the media, the focus has been squarely upon Artificial Intelligence technologies for supporting human argumentation and subsequent automated reasoning with the results. Arguments made outside such software walled gardens have been off the agenda simply because automatic machine understanding of unfettered naturally occurring reasoning has been too hard to tackle. Before 2014, that task -- argument mining -- had been tackled only speculatively and only in specific domains by a very small number of groups such as those at Toronto, Leuven and Dundee. By the end of 2014, more than twenty research labs across the US and EU were gearing up to tackle the problem, there were several international meetings including a regular workshop series at the largest computational linguistics conference, and dozens of results being reported. The reason for this huge upswing in activity lies in maturing technology and the returns available. Opinion mining has transformed the way that market research and PR is carried out, deploying big data analysis techniques to understand the attitudes people hold towards products and brands. Sentiment analysis has had an even greater impact in predicting financial markets by analysing broad moods and perspectives that are expressed in the press. Argument mining is the natural evolution of these technologies, providing a step change in the level of detail available -- moving from not just analysing what opinions people hold, but why they hold the opinions they do. This is why major organisations such as IBM, with whom we are partnering in this project, are so interested in the technology. The Centre for Argument Technology now curates the largest publicly accessible corpus of analysed argument in the world, and has a well known and widely used tool stack for managing datasets, conducting analyses, and visualising the results. This provides a unique platform from which we can both extend existing techniques for argument mining, but also, much more ambitiously, use insights from the philosophy of argumentation and from rhetoric to transform the reliability and applicability of argument mining technology. In particular, we will use the theory of argumentation schemes that characterises stereotypical patterns of reasoning to guide the process of searching for argument components, and the theory of rhetorical figures and tropes as the basis for developing a new class of algorithms for argument recognition. We will thus be transforming bare statistically-driven approaches with detailed theories of structure which can act to define expectations in a way that constrains the machine learning task thereby improving accuracy and applicability. By partnering with IBM and J&L Techology (a domain-specific SME), the project aims not just to radically improve performance of these techniques, establishing the UK's position at the cutting edge, but also to deliver those performance gains to end users.