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Argumentation


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Argumentation


Misinformation can undermine a well-functioning democracy. For example, public misconceptions about climate change can lead to lowered acceptance of the reality of climate change and lowered support for mitigation policies. This study experimentally explored the impact of misinformation about climate change and tested several pre-emptive interventions designed to reduce the influence of misinformation. We found that false-balance media coverage (giving contrarian views equal voice with climate scientists) lowered perceived consensus overall, although the effect was greater among free-market supporters. Likewise, misinformation that confuses people about the level of scientific agreement regarding anthropogenic global warming (AGW) had a polarizing effect, with free-market supporters reducing their acceptance of AGW and those with low free-market support increasing their acceptance of AGW. However, we found that inoculating messages that (1) explain the flawed argumentation technique used in the misinformation or that (2) highlight the scientific consensus on climate change were effective in neutralizing those adverse effects of misinformation. We recommend that climate communication messages should take into account ways in which scientific content can be distorted, and include pre-emptive inoculation messages.


The Minor in Logic, Argumentation, and Writing (LAWR) offers students the opportunity for critical training in logical reasoning, persuasive argumentation, and advanced written communication for professional and academic contexts. The skills gained from the LAWR minor are particularly valuable for the study and practice of law, but also for professional contexts and graduate training where critical thinking and written communication are essential.


And yet, the practice of argumentation has tended to be underemphasized in the context of science education. In response, A Framework for K-12 Science Education and the resulting Next Generation Science Standards list "Engaging in Argument from Evidence" as one of the eight science and engineering practices that all students should consistently engage in.


This short course is designed to help educators think about how the practice of argumentation relates to the practice of explanation, research- and practice-based strategies that can foster rich forms of student argumentation, and how argumentation opportunities can be implemented in more equitable ways.


To learn about the sort of research that goes on in the program and the decades of argumentation research at Windsor that it builds on, visit CRRAR. To join the mailing list for CRRAR events, write: crrar@uwindsor.ca.


All sections of English 225 focus on examining and employing effective academic argumentation. Academic argumentation here refers to the presentation, explanation, and assessment of claims through written reasoning that utilizes appropriate evidence and writing conventions. The course builds on and refines skills from introductory writing courses English 124 and 125, as well as provides a basic introduction to finding, and effectively incorporating research into student writing, for use in a range of future academic contexts.


COMM 131 - Essentials of Argumentation(3 units)Lecture: Theory of argumentation; examination of forms and sources of evidence, inductive and deductive arguments, construction of case briefs, and refutation. Workshop: Develops critical thinking abilities with planned exercises and speeches including construction and presentation of arguments, cases, and refutation.Both grading options.


To investigate what logical mechanisms govern argumentative relations, we hypothesize that governing mechanisms should be able to classify the relations without directly training on relation-labeled data. Thus, we first compile a set of rules specifying logical and theory-informed mechanisms that signal the support and attack relations (3). The rules are grouped into four mechanisms: factual consistency, sentiment coherence, causal relation, and normative relation. These rules are combined via probabilistic soft logic (PSL) (Bach et al., 2017) to estimate the optimal argumentative relations between statements. We operationalize each mechanism by training semantic modules on public datasets so that the modules reflect real-world knowledge necessary for reasoning (4). For normative relation, we build a necessary dataset via rich annotation of the normative argumentation schemes argument from consequences and practical reasoning (Walton et al., 2008), by developing a novel and reliable annotation protocol (5).


A novel and reliable annotation protocol, along with a rich schema, for the argumentation schemes argument from consequences and practical reasoning. We release our annotation manuals and annotated data.1


There has been active research in NLP to understand different mechanisms of argumentation computationally. Argumentative relations have been found to be associated with various statistics, such as discourse markers (Opitz and Frank, 2019), sentiment (Allaway and McKeown, 2020), and use of negating words (Niven and Kao, 2019). Further, as framing plays an important role in debates (Ajjour et al., 2019), different stances for a topic emphasize different points, resulting in strong thematic correlations (Lawrence and Reed, 2017).


Some research adopted argumentation schemes as a framework, making comparisons with discourse relations (Cabrio et al., 2013) and collecting and leveraging data at varying degrees of granularity. At a coarse level, prior studies annotated the presence of particular argumentation schemes in text (Visser et al., 2020; Lawrence et al., 2019; Lindahl et al., 2019; Reed et al., 2008) and developed models to classify different schemes (Feng and Hirst, 2011). However, each scheme often accommodates both support and attack relations between statements, so classifying those relations requires semantically richer information within the scheme than just its presence. To that end, Reisert et al. (2018) annotated individual components within schemes, particularly emphasizing argument from consequences. Based on the logic behind this scheme, Kobbe et al. (2020) developed an unsupervised method to classify the support and attack relations using syntactic rules and lexicons. Our work extends these studies by including other normative schemes (practical reasoning and property-based reasoning) and annotating richer information.


In argumentation, it is often the case that an attacking statement and the claim are not strictly contradictory nor contrary, but the statement contradicts only a specific part of the claim, as in:Claim:Vegan diets are healthy.


Our first dataset is from kialo.com, a collaborative argumentation platform covering contentious topics. Users contribute to the discussion of a topic by creating a statement that either supports or attacks an existing statement, resulting in an argumentation tree for each topic. We define an argument as a pair of parent and child statements, where the parent is the claim and the child is the support or attack statement. Each argument is labeled with support or attack by users and is usually self-contained, not relying on external context, anaphora resolution, or discourse markers.


Among the resulting arguments, 10K are reserved for fitting; 20% or 30% of the rest (depending on the data size) are used for validation and the others for test (Table 4). We increase the validity of the test set by manually discarding non-neutral arguments from the neutral set. We also manually inspect the normativity of claims, and if they occur in the fitting or validation sets too, the corresponding arguments are assigned to the correct sets according to the manual judgments. For normative arguments, we set aside 1,000 arguments for annotating the argumentation schemes (5).


We examined the contribution of each logic task using ablation tests (not shown in the tables). Textual entailment has the strongest contribution across settings, followed by sentiment classification. This contrasts the relatively small contribution of factual consistency in Experiment 1. Moreover, the tasks of normative relation have the smallest contribution for normative arguments and the causality task for non-normative arguments in both datasets. Three of the normative relation tasks take only a statement as input, which is inconsistent with the main task. This inconsistency might cause these tasks to have only small contributions in representation learning. The small contribution of the causality task in both Experiments 1 and 2 suggests large room for improvement in how to effectively operationalize causal relation in argumentation.


We examined four types of logical and theory-informed mechanisms in argumentative relations: factual consistency, sentiment coherence, causal relation, and normative relation. To operationalize normative relation, we built rich annotation schema and data for the argumentation schemes argument from consequences and practical reasoning, too.


Evaluation on arguments from Kialo and Debatepedia revealed the importance of these mechanisms in argumentation, especially normative relation and sentiment coherence. Their utility was further verified in a supervised setting via our representation learning method. Our model learns argument representations that make strong correlations between logical relations and argumentative relations in intuitive ways. Textual entailment was found to be particularly helpful in the supervised setting.


We do not assume that claim-hood and statement-hood are intrinsic features of text spans; we follow prevailing argumentation theory in viewing claims and statements as roles determined by virtue of relationships between text spans.


Assurance cases provide a structured method of explaining why a system has some desired property, for example, that the system is safe. But there is no agreed approach for explaining what degree of confidence one should have in the conclusions of such a case. This report defines a new concept, eliminative argumentation, that provides a philosophically grounded basis for assessing how much confidence one should have in an assurance case argument. This report will be of interest mainly to those familiar with assurance case concepts and who want to know why one argument rather than another provides more confidence in a claim. The report is also potentially of value to those interested more generally in argumentation theory. 59ce067264






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