A Framework for Argument Retrieval: Ranking Argument Clusters by Frequency and Specificity
A Framework for Argument Retrieval: Ranking Argument Clusters by Frequency and Specificity
复制标题
DOI：
10.1007/978-3-030-45439-5_29
复制
发表时间：
2020-03-17
期刊：
影响因子：
--
通讯作者：
Schenkel R
中科院分区：
文献类型：
--
作者：
Dumani L;Neumann PJ;Schenkel R
计算论证最近已成为一个快速增长的研究领域。一个论点由一个主张组成，例如“我们应该放弃化石燃料”，它至少有一个前提支持或攻击，例如“燃烧化石燃料是全球变暖的原因之一”。从信息检索的角度来看，一个有趣的任务，在这种设置是找到最好的支持和攻击的前提，为一个给定的查询索赔从一个大型语料库的参数。由于相同的逻辑前提可以用不同的方式表达，系统需要避免检索重复的结果，因此需要使用某种形式的聚类。在本文中，我们提出了一个原则性的概率排名框架的基础上的思想tf-idf，给定一个查询索赔，首先确定高度相似的索赔语料库中，然后聚类和排名他们的前提，同时考虑集群的索赔以及查询和前提的立场。我们将我们的方法与使用BM 25 F的基线系统进行比较，即使使用BERT框架的原始实现，我们也表现出色。
Computational argumentation has recently become a fast growing field of research. An argument consists of a claim, such as “We should abandon fossil fuels”, which is supported or attacked by at least one premise, for example “Burning fossil fuels is one cause for global warming”. From an information retrieval perspective, an interesting task within this setting is finding the best supporting and attacking premises for a given query claim from a large corpus of arguments. Since the same logical premise can be formulated differently, the system needs to avoid retrieving duplicate results and thus needs to use some form of clustering. In this paper we propose a principled probabilistic ranking framework for premises based on the idea of tf-idf that, given a query claim, first identifies highly similar claims in the corpus, and then clusters and ranks their premises, taking clusters of claims as well as the stances of query and premises into account. We compare our approach to a baseline system that uses BM25F which we outperform even with a primitive implementation of our framework utilising BERT.