Open Argument Mining

公开辩论包括如此之多的论点，以至于合理的决策超出了感兴趣的公众或负责任的专家的认知能力。新论点不断被提出（挑战C1），通常是不完整的（挑战C2），并且需要关于共同事实或先前论点的知识来理解它们（挑战C3）。该项目旨在研究计算方法，i)不断提高其识别正在进行的辩论中的论点的能力，ii)将不完整的论点与先前的论点对齐，并用自动获取的背景知识丰富它们，以及iii)不断扩展语义知识库，其中包含理解论点所需的信息。我们通过结合和推进来自两个研究领域的当前最先进的算法来实现这一目标：参数挖掘和知识图谱构建。为了处理正在进行的辩论中的概念漂移，我们的目标是通过知识感知的终身学习方法来推进论点挖掘方法。我们将研究新的神经网络架构来学习主题不变的论点特征以及论点与辩论主题之间的关系，使用知识图嵌入将语义知识注入神经网络，并利用自我训练来不断扩展训练数据。为了处理不完整的论据，检索到的论据将与已知的论据对齐，并用背景知识丰富。我们将通过链接发现和关键字搜索相结合的方式将论证实体与背景知识链接起来。这种关联的背景知识将被纳入增量聚类方法，将类似的论点分组到论点簇中。这些论证簇之间的论证支持和攻击关系将使用监督学习来确定。我们的目标是通过结合包含百科全书式和常识性知识的当代语义知识库（Babelnet和ConceptNet）和从非结构化Web语料库（Common Crawl）中提取重点知识来自动获取所需的背景知识。为了将这些背景知识集成到机器学习模型中，我们将采用现有的知识嵌入技术来支持增量训练。此外，该项目侧重于开发新的注释方案和新的基准语料库，使我们能够跨主题、文本类型和不同的时间戳评估我们的挖掘和对齐方法。结果将是获得开放论证图的新方法，包括来自多个文本源的语义丰富的相似论证组，这些文本源与支持和攻击关系相关联。为了确保论证风格的广泛覆盖，我们将把我们的方法应用于在线新闻和Twitter消息中经常讨论的不同主题，并使用带注释的黄金数据和基于人群的事后评估进行成分评估。

Open debates include so many arguments that sound decision making exceeds cognitive capabilities of the interested public or responsible experts. New arguments are continuously contributed (challenge C1), are oftenincomplete (C2), and knowledge about common facts or previous arguments is needed to understand them (C3).This project aims at investigating computational methods that i) continuously improve their capability to recognize arguments in ongoing debates, ii) align incomplete arguments with previous arguments and enrichthem with automatically acquired background knowledge, and iii) constantly extend semantic knowledge bases with information required to understand arguments.We achieve this by combining and advancing current state-of-the-art algorithms from the two research fields argument mining and knowledge graph construction. To deal with concept drifts in ongoing debates, we aim to advance argument mining methods with a knowledge-aware lifelong learning approach. We will investigate novel neural architectures for learning topic invariant argument features and the relation between arguments and debate topics, inject semantic knowledge into the neural network using knowledge graph embeddings and leverage self-training to continuously extend the training data. To cope with incomplete arguments, the retrieved arguments will be aligned with known arguments and enriched with background knowledge. We will link the entities of arguments to background knowledge by combining link discovery and keyword search. This linked background knowledge will be incorporated into incremental clustering methods for grouping similar arguments into argument clusters. Argumentative support and attack relations between these argument clusters will be determined using supervised learning. We aim to automatically acquire the required background knowledge by combining contemporary semantic knowledge bases containing encyclopedic and commonsense knowledge (Babelnet and ConceptNet) and focused knowledge extraction from unstructured Web corpora (Common Crawl). To integrate this background knowledge into machine learning models, we are going to adopt existing knowledge embedding techniques to support incremental training. Furthermore, this project focuses on developing novel annotation schemes and new benchmark corpora allowing us to evaluate our mining and alignment methods across topics, text types, and varying timestamps.The outcome will be novel methods for obtaining an Open Argumentation Graph including semantically enriched groups of similar arguments from multiple textual sources linked with support and attack relations. To ensure a wide coverage of argumentation styles, we will apply our methods to different topics frequently discussed in online news and Twitter messages and conduct both component evaluation using annotated gold data and crowd-based post-hoc evaluations.