4,263 research outputs found
DebateKG: Automatic Policy Debate Case Creation with Semantic Knowledge Graphs
Recent work within the Argument Mining community has shown the applicability
of Natural Language Processing systems for solving problems found within
competitive debate. One of the most important tasks within competitive debate
is for debaters to create high quality debate cases. We show that effective
debate cases can be constructed using constrained shortest path traversals on
Argumentative Semantic Knowledge Graphs. We study this potential in the context
of a type of American Competitive Debate, called Policy Debate, which already
has a large scale dataset targeting it called DebateSum. We significantly
improve upon DebateSum by introducing 53180 new examples, as well as further
useful metadata for every example, to the dataset. We leverage the txtai
semantic search and knowledge graph toolchain to produce and contribute 9
semantic knowledge graphs built on this dataset. We create a unique method for
evaluating which knowledge graphs are better in the context of producing policy
debate cases. A demo which automatically generates debate cases, along with all
other code and the Knowledge Graphs, are open-sourced and made available to the
public here: https://github.com/Hellisotherpeople/DebateKGComment: 8 pages, knife-edge reject from EACL 2023 and workshops, System
Demonstration pape
The Role of Human Knowledge in Explainable AI
As the performance and complexity of machine learning models have grown significantly over the last years, there has been an increasing need to develop methodologies to describe their behaviour. Such a need has mainly arisen due to the widespread use of black-box models, i.e., high-performing models whose internal logic is challenging to describe and understand. Therefore, the machine learning and AI field is facing a new challenge: making models more explainable through appropriate techniques. The final goal of an explainability method is to faithfully describe the behaviour of a (black-box) model to users who can get a better understanding of its logic, thus increasing the trust and acceptance of the system. Unfortunately, state-of-the-art explainability approaches may not be enough to guarantee the full understandability of explanations from a human perspective. For this reason, human-in-the-loop methods have been widely employed to enhance and/or evaluate explanations of machine learning models. These approaches focus on collecting human knowledge that AI systems can then employ or involving humans to achieve their objectives (e.g., evaluating or improving the system). This article aims to present a literature overview on collecting and employing human knowledge to improve and evaluate the understandability of machine learning models through human-in-the-loop approaches. Furthermore, a discussion on the challenges, state-of-the-art, and future trends in explainability is also provided
Recommended from our members
Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL
Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N
Countering Misinformation via Emotional Response Generation
The proliferation of misinformation on social media platforms (SMPs) poses a
significant danger to public health, social cohesion and ultimately democracy.
Previous research has shown how social correction can be an effective way to
curb misinformation, by engaging directly in a constructive dialogue with users
who spread -- often in good faith -- misleading messages. Although professional
fact-checkers are crucial to debunking viral claims, they usually do not engage
in conversations on social media. Thereby, significant effort has been made to
automate the use of fact-checker material in social correction; however, no
previous work has tried to integrate it with the style and pragmatics that are
commonly employed in social media communication. To fill this gap, we present
VerMouth, the first large-scale dataset comprising roughly 12 thousand
claim-response pairs (linked to debunking articles), accounting for both
SMP-style and basic emotions, two factors which have a significant role in
misinformation credibility and spreading. To collect this dataset we used a
technique based on an author-reviewer pipeline, which efficiently combines LLMs
and human annotators to obtain high-quality data. We also provide comprehensive
experiments showing how models trained on our proposed dataset have significant
improvements in terms of output quality and generalization capabilities.Comment: Accepted to EMNLP 2023 main conferenc
The Information of Things: A Study on the Potential of Journalism with 5G Technology
This research conducted at the University of Barcelona explores the intersection of emerging technologies such as Artificial Intelligence (AI), mobile 5G, and the Internet of Things (IoT) within journalistic frameworks, aiming to uncover the evolving dynamics in newsrooms influenced by these advancements. By employing methodologies such as bibliographic research for a theoretical exploration of IoT, AI, 5G, and participant observation with the Research Group on Information, Communication, and Culture, the study aims to offer a qualitative insight into the integration of these technologies in journalism. The study focuses on how AI-driven algorithms, 5G connectivity, and IoT devices are collectively transforming journalistic content creation and dissemination, offering new opportunities for enhanced efficiency and creativity while also introducing challenges in real-time data handling, analysis, and distribution. The expected results include a deeper understanding of the impact and potential of these technologies in journalism, emphasizing the need for transparency, accountability, and ethical practices to uphold journalistic integrity and promote informed public discourse amidst these technological advancements
- …