926 research outputs found

    Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

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    Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments.Comment: European Chapter of the Association for Computational Linguistics (EACL 2017

    Argumentation Mining in User-Generated Web Discourse

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    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

    A prosody-based vector-space model of dialog activity for information retrieval

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    Search in audio archives is a challenging problem. Using prosodic information to help find relevant content has been proposed as a complement to word-based retrieval, but its utility has been an open question. We propose a new way to use prosodic information in search, based on a vector-space model, where each point in time maps to a point in a vector space whose dimensions are derived from numerous prosodic features of the local context. Point pairs that are close in this vector space are frequently similar, not only in terms of the dialog activities, but also in topic. Using proximity in this space as an indicator of similarity, we built support for a query-by-example function. Searchers were happy to use this function, and it provided value on a large testset. Prosody-based retrieval did not perform as well as word-based retrieval, but the two sources of information were often non-redundant and in combination they sometimes performed better than either separately.We thank Martha Larson, Alejandro Vega, Steve Renals, Khiet Truong, Olac Fuentes, David Novick, Shreyas Karkhedkar, Luis F. Ramirez, Elizabeth E. Shriberg, Catharine Oertel, Louis-Philippe Morency, Tatsuya Kawahara, Mary Harper, and the anonymous reviewers. This work was supported in part by the National Science Foundation under Grants IIS-0914868 and IIS-1241434 and by the Spanish MEC under contract TIN2011-28169-C05-01.Ward, NG.; Werner, SD.; GarcĂ­a-Granada, F.; SanchĂ­s Arnal, E. (2015). A prosody-based vector-space model of dialog activity for information retrieval. Speech Communication. 68:85-96. doi:10.1016/j.specom.2015.01.004S85966

    The Effect of Task Instructions on Students' Use of Repetition in Argumentative Discourse

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    The reasoning belief of argumentum ad nauseam assumes that when someone repeats something often enough, he or she becomes more convincing. The present paper analyses the use of this strategy by seventh-grade students in an argumentation task. Sixty-five students (mean age: 12.2, SD = 0.4) from a public school in a mid-sized urban environment took part in the study. The students were asked to either argue to convince an opposing partner or argue to reach consensus with an opposing partner on three dilemmas that dealt with energy sources. Data were gathered according to a between-groups design that included one independent variable (argumentative goal: to convince vs. to reach consensus) and one dependent variable (the degree of argumentative repetitions). We predicted that in the condition to convince their partner, the students would use the repetition strategy more often in their attempts to be persuasive. Our findings show that the mean number of argumentative repetitions was significantly higher for the persuasion group for both of the most frequent argumentative structures (claim and claim data). The mean percentage of repeated claims for the persuasion condition was 86.2 vs. 69.0 for the consensus condition. For the claim data, the mean percentage for the persuasion group was 35.2 vs. 24.3 for the consensus group. Also, students in the persuasion group tended to repeat one idea many times rather than repeating many ideas a few times within the same argumentative structure. The results of our study support the hypothesis that the goal of the argumentative task mediates argumentative discourse and, more concretely, the rate of repetitions and the conceptual diversity of the statements. These differences in rates of repetition and conceptual diversity are related to the amount of learning produced by the instructional goal. We apply Mercer's idea that not all classroom argumentation tasks promote learning equally

    ELABORATIVE AND CRITICAL DIALOG: TWO POTENTIALLY EFFECTIVE PROBLEM-SOLVING AND LEARNING INTERACTIONS

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    Recent research on learning individual monologs and collaborative problem solving suggests that students learn best when they are required to be active participants in interactive dialogs. However, some interactive dialogs are more conducive to learning than others. Two dialog patterns that seem to be effective in producing successful problem solving and deep learning are elaborative and critical interactions. The goal of the present study is to evaluate the relative impact of each dialog on learning and problem solving by experimentally manipulating the types of conversations in which dyads engage.Undergraduate participants were randomly assigned to one of four conditions: a singleton control, a dyadic control, an elaborative dyad, or a critical dyad. The domain chosen for the experiment was a bridge optimization task in which individuals or dyads modified a simulated bridge, with the goal of making it as inexpensive as possible.Both problem solving and learning from the simulation were assessed. Performance on the task included a combination of two factors: the quality of the design and the price. Overall learning was measured by the gain from pre- to posttest on isomorphic evaluations, and was further decomposed into text-explicit and inferential knowledge. The results suggest elaboration is easier to train and led to stronger problem solving and learning than the control condition, whereas the critical interactions were more difficult to instruct and led to problem solving and learning equal to the control condition

    The Dimensions of Argumentative Texts and Their Assessment

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    The definition and the assessment of the quality of argumentative texts has become an increasingly crucial issue in education, classroom discourse, and argumentation theory. The different methods developed and used in the literature are all characterized by specific perspectives that fail to capture the complexity of the subject matter, which remains ill-defined and not systematically investigated. This paper addresses this problem by building on the four main dimensions of argument quality resulting from the definition of argument and the literature in classroom discourse: dialogicity, accountability, relevance, and textuality (DART). We use and develop the insights from the literature in education and argumentation by integrating the frameworks that capture both the textual and the argumentative nature of argumentative texts. This theoretical background will be used to propose a method for translating the DART dimensions into specific and clear proxies and evaluation criteria
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