Answering Comparative Questions with Arguments (ACQuA 2.0)

在现代社会中，人们每天都面临着选择问题。在本计画中，我们将自然语言处理与资讯撷取的方法结合联合收割机，设计出一种新的比较问答系统。这样的机器满足了用户对在大量类似对象之间进行选择感到困惑的无处不在的比较信息需求（例如，数码相机、作为旅游目的地的城市或编程语言）。也就是说，机器就像各个领域的人类专家一样，将问题作为输入，并在给定的上下文中建议最佳对象选择，用参数支持答案。类似于与人类专家交谈，与比较辩论机器的交互是使用自然语言进行的。该项目的主要科学挑战是设计一个通用的方法，产生一个良好的覆盖面的对象及其方面在各个领域，以及参数支持或反对对象时，与其他相比，但不依赖于特定领域的资源。一些工业系统能够使用特定的手动创建的数据库在相当狭窄的领域中比较产品。然而，即使是最先进的工业系统在各自的领域都有显着的局限性，满足比较信息的需求，强劲的跨域。目前，还没有一种方法来回答具有合理覆盖范围的一般比较问题，而这正是我们在项目中要解决的问题。为了实现这一目标，我们提出了一种新的方法，包括几个阶段：（1）理解用户的比较问题，（2）从网络规模的文本语料库中检索与问题相关的比较论证结构，（3）从基于维基百科的知识库中收集对象的方面，该知识库丰富有从文本中提取的方面，（4）在比较论元结构和宾语体的基础上对宾语进行比较;（5）生成支持宾语选择的论元;（6）回答呈现。我们技术的关键是一种新颖的论点挖掘方法，该方法对与输入问题相关的文档进行联合检索并从文本中提取复杂的论点结构。我们的项目将把论点挖掘放在回答比较问题的复杂任务的背景下，展示论点挖掘如何能够创建新的语义技术。所提出的比较问题回答方法的突出应用是对话系统，决策支持系统和网络搜索引擎中的直接回答。

In modern society, individuals are faced with choice problems on a daily basis. In this project, we combine methods from natural language processing and information retrieval to design a new kind of argumentative machine for comparative question answering. Such a machine satisfies the ubiquitous comparative information needs of users puzzled by a choice between abundant analogous objects (e.g., digital cameras, cities as travel destinations, or programming languages). Namely, the machine acts like a human expert in a respective field, taking a question as an input and suggesting the best object choice in the given context, supporting the answer with arguments. Similar to talking to human experts, interaction with the comparative argumentative machine is performed using natural language. The main scientific challenge of the project is to design a generic approach that yields a good coverage of objects and their aspects over various domains, as well as arguments for or against objects when compared with others but not relying on domain-specific resources. Some industrial systems are able to compare products in rather narrow domains, using specific manually created databases. Yet, even the most advanced industrial systems in the respective fields all have significant limitations satisfying comparative information needs robustly across domains. Currently, there is no methodology for answering comparative questions in general with reasonable coverage, which is precisely what we address in our project. To reach this goal, we propose a new approach involving several stages: (1) understanding comparative questions by users, (2) retrieving comparative argumentative structures relevant to a question from a web-scale text corpus, (3) gathering aspects of objects from a Wikipedia-based knowledge base enriched with aspects extracted from text, (4) comparison of objects on the basis of comparative argumentative structures and object aspects, (5) generation of arguments supporting the object choice, and (6) answer presentation. Key to our technology is a novel approach to argument mining that performs joint retrieval of documents relevant to the input question and extraction of complex argumentative structures from text. Our project will put argument mining in the context of the complex task of answering comparative questions, showing how argument mining can enable the creation of new kinds of semantic technologies. Prominent applications of the proposed comparative question answering approach are dialogue systems, decision support systems, and direct answers in web search engines.