Implicit Knowledge in Argumentative Texts: An Annotated Corpus

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 2316–2324 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 2316 Implicit Knowledge in Argumentative Texts: An Annotated Corpus Maria Becker, Katharina Korfhage, Anette Frank Institute of Computational Linguistics Heidelberg University (mbecker|korfhage|frank)@cl.uni-heidelberg.de Abstract When speaking or writing, people omit information that seems clear and evident, such that only part of the message is expressed in words. Especially in argumentative texts it is very common that (important) parts of the argument are implied and omitted. We hypothesize that for argument analysis it will be beneficial to reconstruct this implied information. As a starting point for filling such knowledge gaps, we build a corpus consisting of high-quality human annotations of missing and implied information in argumentative texts. To learn more about the characteristics of both the argumentative texts and the added information, we further annotate the data with semantic clause types and commonsense knowledge relations. The outcome of our work is a carefully designed and richly annotated dataset, for which we then provide an in-depth analysis by investigating characteristic distributions and correlations of the assigned labels. We reveal interesting patterns and intersections between the annotation categories and properties of our dataset, which enable insights into the characteristics of both argumentative texts and implicit knowledge in terms of structural features and semantic information. The results of our analysis can help to assist automated argument analysis and can guide the process of revealing implicit information in argumentative texts automatically. Keywords: implicit knowledge, argumentation, annotation, semantic clauses, commonsense knowledge relations, ConceptNet 1. Introduction In everyday communication as well as in written texts peo- ple omit information that seems clear and evident, such that only part of the message needs to be expressed in words (Grice, 1975). While this information can easily be filled in by the hearer, a computational system typically does not possess commonsense or domain-specific knowledge that is needed to reconstruct the implied information. Especially in argumentative texts it is very common that (important) parts of the argument such as warrants are implied and omit- ted (Rajendran et al., 2016; Becker et al., 2017b; Hulpus et al., 2019). This leads us to the assumption that the logic of an argument is in general not fully recoverable from what is explicitly said, and that for argument analysis it will be beneficial to reconstruct such implied information. We aim to fill such gaps by identifying and inserting knowl- edge that connects given statements. To perform this, we want to learn from human-generated data of missing and implied information. This motivates the current work, in which we gather high-quality annotations of implied knowl- edge in the form of simple natural language sentences in En- glish. The annotations are performed on pairs of argumenta- tive units from the Microtexts Corpus (Peldszus and Stede, 2015), a very concise and focused argumentation dataset which is already annotated with argumentative components and relations such as support, rebuttal or undercut. For all unit pairs …

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