Open Argument Mining

公开辩论包括如此多的论点，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。新的论点不断提出（挑战C1），通常是不完整的（C2），并且需要关于共同事实或先前论点的知识来理解它们（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，不断地扩展语义知识库，获取理解论点所需的信息，通过结合和改进论点挖掘和知识图构建这两个研究领域的现有算法来实现这一点。为了处理正在进行的辩论中的概念漂移，我们的目标是推进参数挖掘方法与知识意识终身学习的方法。我们将研究新的神经架构，用于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。为了科普不完整的参数，检索到的参数将与已知的参数对齐，并增加背景知识。我们将通过结合链接发现和关键字搜索将论点的实体与背景知识联系起来。这种关联的背景知识将被纳入增量聚类方法，用于将相似的参数分组到参数集群中。这些论点集群之间的论点支持和攻击关系将使用监督学习来确定。我们的目标是自动获取所需的背景知识，结合当代语义知识库，包含百科知识和常识知识（Babelnet和ConceptNet）和集中的知识提取非结构化的Web语料库（常见的爬行）。为了将这些背景知识整合到机器学习模型中，我们将采用现有的知识嵌入技术来支持增量训练。此外，该项目的重点是开发新的注释方案和新的基准语料库，使我们能够评估我们的挖掘和对齐方法，跨主题，文本类型，和不同的时间戳。结果将是获得开放论证图的新方法，包括语义丰富的组，从多个文本源链接的支持和攻击关系的类似论点。为了确保广泛覆盖论证风格，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，并使用注释的黄金数据和基于人群的事后评估进行组件评估。

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.