Debating Technology for Dialogical Argument

Lawrence, John; Snaith, Mark; Konat, Barbara; Budzyńska, Katarzyna; Reed, Chris

doi:10.1145/3007210

Cited by 11 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We observed varied reasoning patterns and argumentation structures in the study. We noticed instances of debate argument analysis, where quotes are linked to other quotes to map the linear thread of debate to an argument graph, that are similar to existing analytical approaches (e.g., [19]). The process involved many instances of fact checking to establish whether a debater's claim was plausible, and information seeking to find out more about the topic.…”

Section: Evaluation Observationsmentioning

confidence: 99%

“…Debates and debating systems have given rise to many strands of research, often focused on understanding the content of the debate. In this regard, prior work has explored fact checking [12,28], identification of stance [1], highlight identification [36], argument analysis [19], sentiment analysis and segmentation [23], along with second-screen experiences [2,11] and collective assessment (e.g. "the worm" [4]) in live debates , real-time feedback [14], and debate visualisation [29].…”

Section: Brainstorming and Crowdsmentioning

confidence: 99%

See 1 more Smart Citation

Deb8: A Tool for Collaborative Analysis of Video

Carneiro¹,

Nacenta

Toniolo

et al. 2019

Proceedings of the 2019 ACM International Conference on Interactive Experiences for TV and Online Video

View full text Add to dashboard Cite

Public, parliamentary and television debates are commonplace in modern democracies. However, developing an understanding and communicating with others is often limited to passive viewing or, at best, textual discussion on social media. To address this, we present the design and implementation of Deb8, a tool that allows collaborative analysis of video-based TV debates. The tool provides a novel UI designed to enable and capture rich synchronous collaborative discussion of videos based on argumentation graphs that link quotes of the video, opinions, questions, and external evidence. Deb8 supports the creation of rich idea structures based on argumentation theory as well as collaborative tagging of the relevance, support and trustworthiness of the different elements. We evaluated the design of the tool in a study of three groups of three people. We present the results of the study and a reflection on the challenges involved.

show abstract

Section: Evaluation Observationsmentioning

confidence: 99%

Section: Brainstorming and Crowdsmentioning

confidence: 99%

Deb8: A Tool for Collaborative Analysis of Video

Carneiro¹,

Nacenta

Toniolo

et al. 2019

Proceedings of the 2019 ACM International Conference on Interactive Experiences for TV and Online Video

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

“…Some studies developed argumentative dialogue systems through argument mining [19]- [22]. Lawrence et al developed a debate system that utilized an argumentation structure automatically created through an argument mining technique [19]. Rakshit et al developed an arguing bot that chooses utterances from corpora, including debates on the relevant topic; they also explored potential structures of the corpora to expedite choices [20].…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Argumentation Structure for Persuasive Argumentative Dialogue Generation

Sakai

Higashinaka

Yoshikawa

et al. 2020

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Argumentation is a process of reaching a consensus through premises and rebuttals. If an artificial dialogue system can perform argumentation, it can improve users' decisions and ability to negotiate with the others. Previously, researchers have studied argumentative dialogue systems through a structured database regarding argumentation structure and evaluated the logical consistency of the dialogue. However, these systems could not change its response based on the user's agreement or disagreement to its last utterance. Furthermore, the persuasiveness of the generated dialogue has not been evaluated. In this study, a method is proposed to generate persuasive arguments through a hierarchical argumentation structure that considers human agreement and disagreement. Persuasiveness is evaluated through a crowd sourcing platform wherein participants' written impressions of shown dialogue texts are scored via a third person Likert scale evaluation. The proposed method was compared to the baseline method wherein argument response texts were generated without consideration of the user's agreement or disagreement. Experiment results suggest that the proposed method can generate a more persuasive dialogue than the baseline method. Further analysis implied that perceived persuasiveness was induced by evaluations of the behavior of the dialogue system, which was inherent in the hierarchical argumentation structure.

show abstract

“…Each one of the presented research papers exploits data derived from twitter, as the focus of this literature review paper is AM in social media. Web-derived text seems to thrive as a source of AM pipelines, including the task of relation identification, but the source of data usually is a more structured source of text, such as debate forums [86,87,88,89]. Although the information found in social media are characterized as noisy text and it is far from an ideal scenario for AM, the constant generation of content allows to the researchers to conduct research including the time axis in order to understand users' behaviour [72,90] and evaluate their impact beyond the network [91,92].…”

Section: Relations Identificationmentioning

confidence: 99%

The evolution of argumentation mining: From models to social media and emerging tools

Lytos

Λάγκας²,

Sarigiannidis

et al. 2019

Information Processing & Management

View full text Add to dashboard Cite

Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.

show abstract

Debating Technology for Dialogical Argument

Cited by 11 publications

References 42 publications

Deb8: A Tool for Collaborative Analysis of Video

Deb8: A Tool for Collaborative Analysis of Video

Hierarchical Argumentation Structure for Persuasive Argumentative Dialogue Generation

The evolution of argumentation mining: From models to social media and emerging tools

Contact Info

Product

Resources

About