Argument Components Research Articles

Enhancing evidence-based medicine with natural language argumentative analysis of clinical trials

In the latest years, the healthcare domain has seen an increasing interest in the definition of intelligent systems to support clinicians in their everyday tasks and activities. Among others, also the field of Evidence-Based Medicine is impacted by this twist, with the aim to combine the reasoning frameworks proposed thus far in the field with mining algorithms to extract structured information from clinical trials, clinical guidelines, and Electronic Health Records. In this paper, we go beyond the state of the art by proposing a new end-to-end pipeline to address argumentative outcome analysis on clinical trials. More precisely, our pipeline is composed of (i) an Argument Mining module to extract and classify argumentative components (i.e., evidence and claims of the trial) and their relations (i.e., support, attack), and (ii) an outcome analysis module to identify and classify the effects (i.e., improved, increased, decreased, no difference, no occurrence) of an intervention on the outcome of the trial, based on PICO elements. We annotated a dataset composed of more than 500 abstracts of Randomized Controlled Trials (RCT) from the MEDLINE database, leading to a labeled dataset with 4198 argument components, 2601 argument relations, and 3351 outcomes on five different diseases (i.e., neoplasm, glaucoma, hepatitis, diabetes, hypertension). We experiment with deep bidirectional transformers in combination with different neural architectures (i.e., LSTM, GRU and CRF) and obtain a macro F1-score of.87 for component detection and.68 for relation prediction, outperforming current state-of-the-art end-to-end Argument Mining systems, and a macro F1-score of.80 for outcome classification.

Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation

Argument Mining is defined as the task of automatically identifying and extracting argumentative components (e.g., premises, claims, etc.) and detecting the existing relations among them (i.e., support, attack, rephrase, no relation). One of the main issues when approaching this problem is the lack of data, and the size of the publicly available corpora. In this work, we use the recently annotated US2016 debate corpus. US2016 is the largest existing argument annotated corpus, which allows exploring the benefits of the most recent advances in Natural Language Processing in a complex domain like Argument (relation) Mining. We present an exhaustive analysis of the behavior of transformer-based models (i.e., BERT, XLNET, RoBERTa, DistilBERT and ALBERT) when predicting argument relations. Finally, we evaluate the models in five different domains, with the objective of finding the less domain dependent model. We obtain a macro F1-score of 0.70 with the US2016 evaluation corpus, and a macro F1-score of 0.61 with the Moral Maze cross-domain corpus.

‘Enough is enough: this cycle of violence has to come to an end’: Practical reasoning in the editorials during the extradition bill crisis of Hong Kong

Practical reasoning as an important form of argumentation in politics has received limited research attention in critical discourse studies despite the proposal of an analytical model. Focusing on argument development surrounding events related to the extradition bill crisis of Hong Kong, this paper analyzes 48 editorials published in the South China Morning Post during the crisis period adopting Fairclough’s model. A number of recurrent themes have been identified in the corpus in association with the four argument components within the model – Claims, Goals, Values and Circumstances – which may increase understanding of the social contexts of the extradition bill crisis. It is further argued that, although Fairclough’s model may not capture the arguments in individual editorials comprehensively, the model can be usefully applied to analyze a collection of interrelated editorials to reveal more insights about an ongoing political crisis.

Argument Mining on Twitter: A survey

Abstract In the last decade, the field of argument mining has grown notably. However, only relatively few studies have investigated argumentation in social media and specifically on Twitter. Here, we provide the, to our knowledge, first critical in-depth survey of the state of the art in tweet-based argument mining. We discuss approaches to modelling the structure of arguments in the context of tweet corpus annotation, and we review current progress in the task of detecting argument components and their relations in tweets. We also survey the intersection of argument mining and stance detection, before we conclude with an outlook.

Calculating the Strength of Rhetorical Arguments in Persuasive Negotiation Dialogues

Rhetorical arguments are used in negotiation dialogues when a proponent agent tries to persuade his opponent to accept a proposal more readily. When more than one argument is generated, the proponent must compare them in order to select the most adequate for his interests. A way of comparing them is by means of their strength values. Related work propose a calculation based only on the components of the rhetorical arguments, i.e., the importance of the opponent's goal and the certainty level of the beliefs that make up the argument. This work aims to propose a model for the calculation of the strength of rhetorical arguments, which is inspired on the pre-conditions of credibility and preferability stated by Guerini and Castelfranchi. Thus, we suggest the use of two new criteria to the strength calculation: the credibility of the proponent and the status of the opponent's goal in the goal processing cycle. The model is empirically evaluated and the results demonstrate that the proposed model is more efficient than previous works in terms of number of exchanged arguments and number of reached agreements.

Exploring the impact of the framing of a laboratory experiment on the nature of student argumentation

Research on student argumentation in chemistry laboratories has mainly focused on evaluating the quality of students’ arguments and analyzing the structure of such arguments (i.e. claims, evidence, and rationale). Despite advances in these areas, little is known about the impact of activity framing on the nature of student argumentation in laboratory settings. In this research study, we analyzed the arguments generated by college organic chemistry students working on a substitution reaction experiment that was framed in two distinct ways: predict-verify and observe-infer. The arguments constructed by students in their post-laboratory reports under each laboratory frame were characterized by paying attention to both domain-specific and domain-general features. Our analysis revealed significant differences in the chemical concepts and ideas that students under the two conditions invoked, as well as in the level of integration, specificity, alignment, and type of reasoning observed within and across different argument components. Our findings highlight the importance of paying attention to how experiments are framed in terms of the goals, procedures, information, and tools available to students as these decisions can have a major impact on the nature of the claims students make, their use of evidence, and the approach to reasoning that they follow.

Персономен Шевченко як маркер бінарних опозицій у творах Дмитра Донцова

Systematization of linguistic, political science and literature data is an important step in the study of the D. Dontsov’s linguistic portrait. Argumentative component in the texts furthers formation of an outlook.

Development and Implementation of Students' Scientific Argumentation Skills Test in Acid-Base Chemistry

This study aimed to develop and validate Scientific Argumentation Skills Test (SAST) and investigate the 11th-grade students' performance in scientific argumentation skills on acid-base chemistry. The research design used was research and development, followed by descriptive research. Research and development were carried out to obtain an instrument of SAST, and descriptive research was used to describe students' argumentation skills in acid-base chemistry. Participants in this study were 328 11th-grade students of state high schools in East Java, Indonesia. The research and development of SAST consisted of five steps, namely literature review, items development, expert judgment, pilot project, and finalization of instruments. Expert judgment involved three chemistry education experts, while the pilot project involved 151 students, and the identification of students' scientific argumentation skills involved 177 students. Data about expert assessments, student responses to the pilot project, and student answers to the application of SAST were analyzed descriptively. The SAST produced in the research and development steps consisted of parts A (10 items) and part B (7 items), with Cronbach's alpha reliability coefficients of 0.888 and 0.758. The students' performance in scientific argumentation skills showed that the average score of students' performance to determine the argument's components was 80.53% (excellent category). The average score of students' performance to write an argument was 55.42% (moderate category). The implication of the study that the students' scientific argumentation skills must be explicitly trained in learning.

Rhetorical Approach to Legal Arguing and Legal Argumentation

The article is devoted to legal argumentation, namely to its research by rhetorical approach. The aim of the article is to determine characteristic features of rhetorical approach to legal argumentation.Rhetorical approach to the research of legal argumentation should include philosophical, theoretical, empirical components. Philosophical component of legal argumentation research consists in the anthropological conception of rationality i.e. the philosophical axiomatic idea about rationality of legal argumentation if in is acknowledged by the members of legal community.Rhetorical theoretical model of legal argumentation ensures persuasion of legal audience via legal argumentation. Rhetorical approach deals with legal argumentation mainly in the “context of discovery”, i.e. with legal arguing. Rhetorical approach to legal arguing implies specific standard of soundness of the argumentation – persuasion standard. Empirical component of legal argumentation includes description of arguments and persuasive techniques. Rhetorical approach insures interaction between natural law and positive law.Rhetorical approach to legal argumentation lets us assume some ontological issues concerning legal argumentation. Legal argumentation is considered as the mode of impact on the audience via application of communicative and interactional means which persuasive force depends on the contextof argumentative legal discourse. Legal argumentation is the result of such impact embodied in the system of arguments to persuade the legal audience in the arguability of the standpoint.

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.

Improving students’ arguments through collaborative learning

The ability to produce argumentation is crucial for university students’ academic achievement and upcoming careers. Developing and organizing ideas to create an argument, requires students’ thinking skills. Attempts to improve students’ written arguments through collaborative learning have been conducted in numerous studies. However, there are few studies on improving students’ spoken arguments through collaborative learning, especially in Bahasa Indonesia. Therefore, this research aims to investigate how students’ arguments improve through collaborative learning. This research uses a one group pre-test post-test design method. The participants were 21 undergraduate students from a university in Indonesia. Students were presented a video of Indonesia’s 2019 presidential debate and an argumentation model as the stimulus. Students’ arguments were recorded, transcribed, translated and analyzed to detect any improvements from the pre-test compared to the post-test. Findings show that through collaborative learning students can produce more argument component parts leading to a higher level of argument. They are also able to use their thinking skills of remember, understand, apply and analyze in the learning process. As conclusion, collaborative learning can be considered as a classroom strategy in improving students’ arguments, from producing claim, data and warrant in arguments to improving awareness in applying other component parts of backing, rebuttal and qualifier. However, there are no detections of a new component part found in students’ argument or an implementation of the highest order thinking skills, that is create.

Parsing argued opinion structure in Twitter content

In this paper, we address the opinion argumentation mining issue from Twitter data with the objective of further analyzing Twitter users’ preferences and motivations. After introducing the argued opinion definition and its different elements, we propose an argued opinion mining system called TOMAS where we present an end-to-end approach to parse the structure of the argued opinion in order to identify its elements. Our suggested system consists of four consecutive sub-tasks, namely: (1) opinion-topic detection, (2) argumentative opinions identification, (3) argument components detection, and (4) argumentative relation recognition. The proposed system optimizes the argued opinion structure using different classification models. The experimental study is conducted on the MC2 Lab CLEF2017 tweets corpus while considering various comparative baselines. We highlight that our system significantly outperforms the majority baselines and significantly outperforms challenging existing approaches.

Argumentative relation identification in academic texts

The argumentation in academic writings is necessary to clearly communicate the ideas of the students. The relations between argumentative components are an essential part since this shows the contrast or support of the presented ideas. In this paper, we present two approaches to relation identification between pairs of components. In the first, we detect initially which components are related, to later classify them in support or attack relation. In the second approach, we identify directly which components have a support relation. For these approaches, we employed machine learning techniques with representations of several lexical, syntactic, semantic, structural and indicator features. Experiments in argumentative sections of academic theses showed that the models achieve encouraging results solving the task, and revealing the argumentative structures prevailing in student writings.

Mining arguments in scientific abstracts with discourse-level embeddings

Argument mining consists in the automatic identification of argumentative structures in texts. In this work we leverage existing discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task. We propose a new annotation schema and use it to augment a corpus of computational linguistics abstracts that had previously been annotated with discourse units and relations. Our initial experiments with the enriched corpus confirm the potential value of incorporating discourse information in argument mining tasks. In order to tackle the limitations posed by the lack of corpora containing both discourse and argumentative annotations we explore two transfer learning approaches in which discourse parsing is used as an auxiliary task when training argument mining models. In this case, as no discourse information is used as input, the resulting models could be used to predict the argumentative structure of unannotated texts.

Examining the effect of reporting based on science writing heuristic approach on written argument quality in general biology laboratory

ABSTRACT The aim of this study was to investigate pre-service science teachers’ development of the quality of written arguments using science writing heuristic approach and pre-service science teachers’ views about the science writing heuristic and its argument components in a General Biology Laboratory Course II. The study was carried out with 21 sophomore pre-service science teachers enrolled at a public university. A case study design was used and eight science writing heuristic approach experiments were implemented over 14 weeks. Data included a grading rubric to analyze pre-service science teachers’ written arguments and a survey to explore their views about the science writing heuristic approach and its components. Data gathered from the grading rubric were analyzed using ANOVA and data gathered from the survey were analyzed using content analysis. The results of the ANOVA analysis indicated that the quality of pre-service science teachers’ argumentative writing significantly increased over time. The survey results highlighted that the science writing heuristic approach increased pre-service science teachers’ research skills and contributed to their permanent learning. Moreover, the pre-service science teachers were able to make connections between components of the science writing heuristic approach and claim, evidence and reflection components were the most useful and challenging components.

ARGUMENTATION OF SENIOR HIGH SCHOOL STUDENTS ON PHYSICS INSTRUCTION BASED INQUIRY

Argumentation as the one of important skills must be included in the science learning. With argumentation skills, students can show their opinion include evidence and justification logically. Argumentation is important for students, but it seldom integrate in physic learning based inquiry. Participation of students who decrease in the learning can influence to argumentation’s ability of students. Physic learning based inquiry which include argumentation section can grow up the action students to give argumentation. Component of argumentation’s ability are evidence and justification for argument, counter argument, and rebuttal. This research is conduct for three meetings on the fluid static’s chapter in Senior High School of one Gambiran. Students’ argumentation skills measure from argumentation section and students’ worksheets has consisted component’s of argumentation. Purpose of this research is describe of students’ argumentation skills on physics instruction based inquiry. Result of this research is shown that students’ argumentation skills increase on last meeting of physics instruction. Participation of students give argumentation influenced by topic of the problems. Keywords: argumentation, participation, inquiry, physic, learningÂ

Talk About Evidence During Argumentation

ABSTRACT Evidence is widely recognized as an essential component of argumentation. Existing research has primarily focused on students’ use of evidence to construct explanations or claims. In the present study, 54 11- to 12--year-old Chinese students participated in an extended discourse-based argumentation curriculum, along with an equivalent nonparticipating control group consisted of 50 students. We identified and traced progression in students’ meta-talk about evidence during peer-to-peer argumentive discourse and found that meta-talk grew more frequent over time, became increasingly focused on evaluating the source of evidence, and became better sustained over successive turns. Separate pre- and postassessments suggested that participants had become more advanced in epistemological understanding, manifested in a shift away from absolutist thinking, and were more likely to endorse the values of argumentive discourse. Implications for epistemic vigilance in the Information Age are discussed.

'Digital authoring support for argumentative writing: what does it change?

C-SAW (Computer-Supported Argumentative Writer) is an online authoring software embodying design principles derived from theories on written argumentation, self-regulation and conceptual change as well as feedback from practitioners and users, in line with a design-based research approach. Designed to scaffold writing processes, C-SAW is intended as additional support in instructional designs using argumentative writing for learning. This article presents the results of a mixed-method study comparing undergraduate students writing with C-SAW or a text editor. Outcome measures included the number of arguments and the degree of their completion, knowledge of argument components, topic knowledge and changes in epistemic beliefs. Participants writing with C-SAW elaborated arguments to a greater degree, but there were otherwise no significant differences between conditions for other measured outcomes. Furthermore, results were influenced by informal reasoning skills that outweighed the effects of condition. These results are discussed with respect to the difficulties of studying the effects of digital tools on writing and learning in controlled first-use contexts and the importance of developing instructional designs with explicit learning outcomes that are aligned to the instructional principles embedded in digital tools.

Argument corpus development and argument component classification: A study in academic Spanish

Abstract Academic writing is a complex task that requires an author to be skilled in argumentation. The goal of an academic author is to communicate ideas clearly and to convince the reader of the veracity of their claims. However, few students in lower level courses are good arguers, as this is a skill that takes time to master. With the aim of contributing to the development of this skill, we introduce a freely available annotated corpus to support research on argumentation in Spanish. To build the corpus, we developed an annotation guide to identify argumentation in paragraphs. The guide also outlines how to annotate segments of sentences as either claims or premises and how to indicate relations (support or attack) between such components. Then, an annotated corpus of 444 sections was created, and an almost perfect interannotator agreement rate was achieved for a number of its components. After its construction, the corpus was used to perform an exploratory analysis, which aimed to identify the amount of argumentation in each section as well as the patterns commonly used for argument identification. Thereafter, experiments were conducted to automatically classify argumentative components using lexical, semantic, discourse, structural, and sentiment features. A Document Occurrence Representation was also tested on the corpus constituting a novel approach in the representation of documents for automatic argument detection. The results of these experiments to automatically classify argumentative components in Spanish are very promising and seem to support the idea that linguistic feature engineering plays a crucial role in text classification.

Instructional patterns for the teaching and learning of argumentative writing in high school English language arts classrooms

Argumentative writing has long been considered an essential skill for disciplinary learning. For researchers and curriculum developers to develop ecologically valid instructional approaches to argumentative writing, a pivotal prerequisite is the understanding of how teachers use various instructional methods in tandem to teach different argumentative components. This exploratory study identified instructional patterns for the teaching and learning of argumentative writing by observing 187 English language arts class sessions taught by 31 highly regarded high school English language arts teachers (529 students; 40% of the students were males). Multidimensional scaling identified three instructional patterns that vary in the level of teacher centeredness and dialogic interaction. These instructional patterns may reflect the occurrence of explicit teaching, dialogic learning, and in-class writing that was sometimes accompanied with teacher conferencing or coaching. Common across all of these practices was the teaching of claim and evidence. Warranting, counterargument, and response to counterargument, which are more complex forms of argumentation, tended to be taught by instructional practices involving low- to mid-level teacher centeredness and high-level dialogic interaction (e.g., small grouping, discussion). Overall, our findings highlight the gaps between what researchers suggest as effective approaches to teaching argumentative writing and how argumentative writing is currently taught in classrooms.