VivesDebate: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament

The application of the latest Natural Language Processing breakthroughs in computational argumentation has shown promising results, which have raised the interest in this area of research. However, the available corpora with argumentative annotations are often limited to a very specific purpose or are not of adequate size to take advantage of state-of-the-art deep learning techniques (e.g., deep neural networks). In this paper, we present VivesDebate, a large, richly annotated and versatile professional debate corpus for computational argumentation research. The corpus has been created from 29 transcripts of a debate tournament in Catalan and has been machine-translated into Spanish and English. The annotation contains argumentative propositions, argumentative relations, debate interactions and professional evaluations of the arguments and argumentation. The presented corpus can be useful for research on a heterogeneous set of computational argumentation underlying tasks such as Argument Mining, Argument Analysis, Argument Evaluation or Argument Generation, among others. All this makes VivesDebate a valuable resource for computational argumentation research within the context of massive corpora aimed at Natural Language Processing tasks.

An argument analysis of cognate objects in Dschang (Yemba)

Focusing on the Foto dialect of Dschang (Yemba), an understudied Grassfields Bantu language spoken in Cameroon, this paper offers a cross-linguistic perspective on Cognate Objects (CO). An argument analysis of Dschang COs is supported by both cross-linguistic comparison, e.g. forms of corresponding wh-questions, the compatibility with strong determiners, quantifiers and possessors, and the ability to be pronominalized and relativized, and Dschang-internal evidence including word order variations and tonal marking in object position.

Integrating Workforce Competencies into Introductory Religion Writing Assignments

Taking up the suggestions made by Eugene V. Gallagher and Joanne Maguire in their article, “Teaching Religion to Undergraduates in the 2020s: A Preliminary Reconnaissance,” this essay addresses one means of thinking about writing assignments in introductory religion courses at the undergraduate level with “broad goals” and “institutional mission” in mind. The essay begins with a description of the institutional context and proceeds to describe an argument analysis writing assignment for a general education religion course that attempt to draw out the “workforce competencies” developed in the exercise. Framing assignments explicitly in terms of the workforce skills students will hone offers teachers the ability to display the transferrable skills they help students develop and provides an avenue to connect assignments to the institution’s mission statement.

On the absence of backsplash analogues to NGC 3109 in the ΛCDM framework

The dwarf galaxy NGC 3109 is receding 105 km/s faster than expected in a ΛCDM timing argument analysis of the Local Group and external galaxy groups within 8 Mpc. If this few-body model accurately represents long-range interactions in ΛCDM, this high velocity suggests that NGC 3109 is a backsplash galaxy that was once within the virial radius of the Milky Way and was slingshot out of it. Here, we use the Illustris TNG300 cosmological hydrodynamical simulation and its merger tree to identify backsplash galaxies. We find that backsplashers as massive (≥4.0 × 1010M⊙) and distant (≥1.2 Mpc) as NGC 3109 are extremely rare, with none having also gained energy during the interaction with their previous host. This is likely due to dynamical friction. Since we identified 13225 host galaxies similar to the Milky Way or M31, we conclude that postulating NGC 3109 is a backsplash galaxy causes >3.96σ tension with the expected distribution of backsplashers in ΛCDM. We show that the dark matter only version of TNG300 yields much the same result, demonstrating its robustness to how the baryonic physics is modelled. If instead NGC 3109 is not a backsplasher, consistency with ΛCDM would require the timing argument analysis to be off by 105 km/s for this rather isolated dwarf, which we argue is unlikely. We discuss a possible alternative scenario for NGC 3109 and the Local Group satellite planes in the context of MOND, where the Milky Way and M31 had a past close flyby 7 − 10 Gyr ago.

Frege’s New Logic and the Function/Argument Regimentation

Frege says his new logical language is not designed to play the role of natural language. It is, rather, a tool designed for specific scientific purposes. The role he assigns to his logical language forces him to abandon traditional subject–predicate analyses of statements in favor of a new kind of analysis in terms of function and argument. But the function-argument analysis, in its original version, gives rise to several problems, among them the problem about identity with which Frege begins “On Sinn and Bedeutung.” This chapter traces through the problems with the original version of the new logical language and Frege’s later solutions. A key part of these solutions is to take sentences to be object names. And, while this may be problematic in an account of the workings of natural languages, it is unproblematic in a language that plays the role of Frege’s logical language.

Argument annotation and analysis using deep learning with attention mechanism in Bahasa Indonesia

Argumentation mining is a research field which focuses on sentences in type of argumentation. Argumentative sentences are often used in daily communication and have important role in each decision or conclusion making process. The research objective is to do observation in deep learning utilization combined with attention mechanism for argument annotation and analysis. Argument annotation is argument component classification from certain discourse to several classes. Classes include major claim, claim, premise and non-argumentative. Argument analysis points to argumentation characteristics and validity which are arranged into one topic. One of the analysis is about how to assess whether an established argument is categorized as sufficient or not. Dataset used for argument annotation and analysis is 402 persuasive essays. This data is translated into Bahasa Indonesia (mother tongue of Indonesia) to give overview about how it works with specific language other than English. Several deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit) are utilized for argument annotation and analysis while HAN (Hierarchical Attention Network) is utilized only for argument analysis. Attention mechanism is combined with the model as weighted access setter for a better performance. From the whole experiments, combination of deep learning and attention mechanism for argument annotation and analysis arrives in a better result compared with previous research.

Libri ad Nauseam: The Critical Thinking Textbook Glut

Critical thinking instructors are faced with an overwhelming number of textbooks to choose from for their courses. Many of these texts do not reflect an awareness of current scholarship in critical thinking and informal logic. I argue that instructors should only adopt textbooks that reflect a sound theoretical understanding of the topic by acknowledging the central role of critical thinking dispositions, offering a more nuanced approach to the teaching of fallacies and of inference, stressing dialectic and argument revision, focusing on the analysis and evaluation of real arguments, and broadening the scope of critical thinking beyond argument analysis and evaluation. To support instructors in this regard, I critique one popular textbook now in its sixth edition that does not satisfy many of these criteria, Munson and Black (2012), and applaud one new textbook that I find does succeed on many of these fronts, Bailin and Battersby (2010).