Exploring the interplay of mode of discourse and proficiency level in ESL writing performance

Recent theory in discourse and practice in rhetoric has suggested that writers require different skills and strategies when writing for different purposes, and in using different genres and modes (Kinneavy, 1972; Carrell and Connor, 1991) in writing. The importance of taking into account these various aspectual skills and forms of writing is recognised in teaching (e.g. Scarcella and Oxford, 1992), and in the assessment of writing (e.g. Odell and Cooper, 1980). For instance, Odell and Cooper argued that any claims about writing ability cannot be made until students’ performance on a variety of writing tasks has been examined. Thus, the issue of what writing task(s) are to be included in a test is crucial, since a task will be regarded as useless if it does not provide the basis for making generalisations regarding an individual’s writing ability. This paper presents the findings of a study on the effects of mode of discourse on L2 writing performance as well as the interplay between learner variable, namely, proficiency level and task variable, mode of discourse amongst Malaysian upper secondary ESL learners. The findings provide some evidence for the need to re-examine issues of reliability and validity in test practice of manipulating variables in the design of assessment tasks to evaluate ESL writing performance. Given the status and complexity of the writing skill, it stands to reason that studies into this area will continue to shed light onto how best the construct can be understood, taught and tested to give a fair chance for language learners to exhibit their true ability and be reliably reported on

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Branding and strategic maneuvering in the Romanian presidential election of 2004: A critical discourse-analytical and pragma-dialectical perspective

In this paper I analyse differences in the legitimation strategies used by and on behalf of the two presidential candidates in the elections of December 2004 in Romania, using a combination of Critical Discourse Analysis and pragma-dialectics. These differences are seen to lie primarily in the varieties of populist discourse that were drawn upon in the construction of legitimizing arguments for both candidates: a paternalist type vs. a radical, anti-political type of populism. I relate the success of the latter type to more effective strategic maneuvering in argumentation, part of more effective branding strategies in general, but also to existing types of political culture amongst the electorate and to social, economic circumstances. In CDA terms, I discuss the "Băsescu brand" as involving choices at the level of discourse, genre and style; in pragma-dialectical terms, I view its success as partly the effect of successful strategic maneuvering. I also place the success of this brand within the Romanian context at the end of 2004, where often questionable populist electoral messages were perceived as reasonable and acceptable, as fitting adjustments to the situation and even as means of optimizing the deliberative situation of the electorate

Assessing relevance

This paper advances an approach to relevance grounded on patterns of material inference called argumentation schemes, which can account for the reconstruction and the evaluation of relevance relations. In order to account for relevance in different types of dialogical contexts, pursuing also non-cognitive goals, and measuring the scalar strength of relevance, communicative acts are conceived as dialogue moves, whose coherence with the previous ones or the context is represented as the conclusion of steps of material inferences. Such inferences are described using argumentation schemes and are evaluated by considering 1) their defeasibility, and 2) the acceptability of the implicit premises on which they are based. The assessment of both the relevance of an utterance and the strength thereof depends on the evaluation of three interrelated factors: 1) number of inferential steps required; 2) the types of argumentation schemes involved; and 3) the implicit premises required

Parsing Argumentation Structures in Persuasive Essays

In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using integer linear programming. We show that our model considerably improves the performance of base classifiers and significantly outperforms challenging heuristic baselines. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement. This corpus and the annotation guidelines are freely available for ensuring reproducibility and to encourage future research in computational argumentation.Comment: Under review in Computational Linguistics. First submission: 26 October 2015. Revised submission: 15 July 201

Analytic frameworks for assessing dialogic argumentation in online learning environments

Over the last decade, researchers have developed sophisticated online learning environments to support students engaging in argumentation. This review first considers the range of functionalities incorporated within these online environments. The review then presents five categories of analytic frameworks focusing on (1) formal argumentation structure, (2) normative quality, (3) nature and function of contributions within the dialog, (4) epistemic nature of reasoning, and (5) patterns and trajectories of participant interaction. Example analytic frameworks from each category are presented in detail rich enough to illustrate their nature and structure. This rich detail is intended to facilitate researchers’ identification of possible frameworks to draw upon in developing or adopting analytic methods for their own work. Each framework is applied to a shared segment of student dialog to facilitate this illustration and comparison process. Synthetic discussions of each category consider the frameworks in light of the underlying theoretical perspectives on argumentation, pedagogical goals, and online environmental structures. Ultimately the review underscores the diversity of perspectives represented in this research, the importance of clearly specifying theoretical and environmental commitments throughout the process of developing or adopting an analytic framework, and the role of analytic frameworks in the future development of online learning environments for argumentation

What changed your mind : the roles of dynamic topics and discourse in argumentation process

In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the in- creasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful -- topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations

A framework to analyze argumentative knowledge construction in computer-supported collaborative learning

Computer-supported collaborative learning (CSCL) is often based on written argumentative discourse of learners, who discuss their perspectives on a problem with the goal to acquire knowledge. Lately, CSCL research focuses on the facilitation of specific processes of argumentative knowledge construction, e.g., with computer-supported collaboration scripts. In order to refine process-oriented instructional support, such as scripts, we need to measure the influence of scripts on specific processes of argumentative knowledge construction. In this article, we propose a multi-dimensional approach to analyze argumentative knowledge construction in CSCL from sampling and segmentation of the discourse corpora to the analysis of four process dimensions (participation, epistemic, argumentative, social mode)