Argument maps structure discourse into nodes in a tree with each node being an argument that supports or opposes its parent argument. This format is more comprehensible and less redundant compared to an unstructured one. Exploring those maps and maintaining their structure by placing new arguments under suitable parents is more challenging for users with huge maps that are typical in online discussions. To support those users, we introduce the task of node placement: suggesting candidate nodes as parents for a new contribution. We establish an upper-bound of human performance, and conduct experiments with models of various sizes and training strategies. We experiment with a selection of maps from Kialo, drawn from a heterogeneous set of domains. Based on an annotation study, we highlight the ambiguity of the task that makes it challenging for both humans and models. We examine the unidirectional relation between tree nodes and show that encoding a node into different embeddings for each of the parent and child cases improves performance. We further show the few-shot effectiveness of our approach.

Computational argumentation is an interdisciplinary research field, connecting Natural Language Processing (NLP) to other disciplines such as the social sciences. This tutorial will focus on a task that recently got into the center of attention in the community: argument quality assessment, that is, what makes an argument good or bad? We structure the tutorial along three main coordinates: (1) the notions of argument quality across disciplines (how do we recognize good and bad arguments?), (2) the modeling of subjectivity (who argues to whom; what are their beliefs?), and (3) the generation of improved arguments (what makes an argument better?). The tutorial highlights interdisciplinary aspects of the field, ranging from the collaboration of theory and practice (e.g., in NLP and social sciences), to approaching different types of linguistic structures (e.g., social media versus parliamentary texts), and facing the ethical issues involved (e.g., how to build applications for the social good). A key feature of this tutorial is its interactive nature: We will involve the participants in two annotation studies on the assessment and the improvement of quality, and we will encourage them to reflect on the challenges and potential of these tasks.

Human moderation is commonly employed in deliberative contexts (argumentation and discussion targeting a shared decision on an issue relevant to a group, e.g., citizens arguing on how to employ a shared budget). As the scale of discussion enlarges in online settings, the overall discussion quality risks to drop and moderation becomes more important to assist participants in having a cooperative and productive interaction. The scale also makes it more important to employ NLP methods for(semi-)automatic moderation, e.g. to prioritize when moderation is most needed. In this work, we make the first steps towards (semi-)automatic moderation by using state-of-the-art classification models to predict which posts require moderation, showing that while the task is undoubtedly difficult, performance is significantly above baseline. We further investigate whether argument quality is a key indicator of the need for moderation, showing that surprisingly, high quality arguments also trigger moderation. We make our code and data publicly available.

This survey builds an interdisciplinary picture of Argument Mining (AM), with a strong focus on its potential to address issues related to Social and Political Science. More specifically, we focus on AM challenges related to its applications to social media and in the multilingual domain, and then proceed to the widely debated notion of argument quality. We propose a novel definition of argument quality which is integrated with that of deliberative quality from the Social Science literature. Under our definition, the quality of a contribution needs to be assessed at multiple levels: the contribution itself, its preceding context, and the consequential effect on the development of the upcoming discourse. The latter has not received the deserved attention within the community. We finally define an application of AM for Social Good: (semi-)automatic moderation, a highly integrative application which (a) represents a challenging testbed for the integrated notion of quality we advocate, (b) allows the empirical quantification of argument/deliberative quality to benefit from the developments in other NLP fields (i.e. hate speech detection, fact checking, debiasing), and (c) has a clearly beneficial potential at the level of its societal thanks to its real-world application (even if extremely ambitious).

Quantification (see e.g. Peters and Westerst ̊ahl, 2006) is probably one of the most extensively studied phenomena in formal semantics. But because of the specific representation of meaning assumed by modeltheoretic semantics (one where a true model of the world is a priori available), research in the area has primarily focused on one question: what is the relation of a quantifier to the truth value of a sentence? In contrast, relatively little has been said about the way the underlying model comes about, and its relation to individual speakers’ conceptual knowledge. In this paper, we make a first step in investigating how native speakers of English model relations between non-grounded sets, by observing how they quantify simple statements. We first give some motivation for our task, from both a theoretical linguistic and computational semantic point of view (§2). We then describe our annotation setup (§3) and follow on with an analysis of the produced dataset, conducting a quantitative evaluation which includes inter-annotator agreement for different classes of predicates (§4). We observe that there is significant agreement between speakers but also noticeable variations. We posit that in settheoretic terms, there are as many worlds as there are speakers (§5), but the overwhelming use of underspecified quantification in ordinary language covers up the individual differences that might otherwise be observed.

This paper describes a Name Matching Evaluation Laboratory that is a joint effort across multiple projects. The lab houses our evaluation infrastructure as well as multiple name matching engines and customized analytical tools. Included is an explanation of the methodology used by the lab to carry out evaluations. This methodology is based on standard information retrieval evaluation, which requires a carefully-constructed test data set. The paper describes how we created that test data set, including the “ground truth” used to score the systems’ performance. Descriptions and snapshots of the lab’s various tools are provided, as well as information on how the different tools are used throughout the evaluation process. By using this evaluation process, the lab has been able to identify strengths and weaknesses of different name matching engines. These findings have led the lab to an ongoing investigation into various techniques for combining results from multiple name matching engines to achieve optimal results, as well as into research on the more general problem of identity management and resolution.