La Semiótica como eslabón emancipador : los movimientos sociales entre ritmo, cuerpo y contagio

Fil: Galassi, Paolo. Universidad de Bologn

Argumentative Link Prediction using Residual Networks and Multi-Objective Learning.

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. We propose a domain-agnostic method that makes no assumptions on document or argument structure. We evaluate our method on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge

Attention in Natural Language Processing

Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures in natural language processing, with a focus on those designed to work with vector representations of the textual data. We propose a taxonomy of attention models according to four dimensions: the representation of the input, the compatibility function, the distribution function, and the multiplicity of the input and/or output. We present the examples of how prior information can be exploited in attention models and discuss ongoing research efforts and open challenges in the area, providing the first extensive categorization of the vast body of literature in this exciting domain

An Argumentative Dialogue System for COVID-19 Vaccine Information

open3noDialogue systems are widely used in AI to support timely and interactive communication with users. We propose a general-purpose dialogue system architecture that leverages computational argumentation to perform reasoning and provide consistent and explainable answers. We illustrate the system using a COVID-19 vaccine information case study.openFazzinga, Bettina; Galassi, Andrea; Torroni, PaoloFazzinga, Bettina; Galassi, Andrea; Torroni, Paol

Multi-Task Attentive Residual Networks for Argument Mining

We explore the use of residual networks and neural attention for argument mining and in particular link prediction. The method we propose makes no assumptions on document or argument structure. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble. We evaluate it on a challenging data set consisting of user-generated comments, as well as on two other datasets consisting of scientific publications. On the user-generated content dataset, our model outperforms state-of-the-art methods that rely on domain knowledge. On the scientific literature datasets it achieves results comparable to those yielded by BERT-based approaches but with a much smaller model size.Comment: 12 pages, 2 figures, submitted to IEEE Transactions on Neural Networks and Learning System

The Immutability of Artwork in the Age of Digital Reproduction: NFT from the insiders\u27 perspective

A Non-Fungible Token (NFT) is a combination of a digital object and its blockchain-based certificate that promise to solve problems of authentic- ity and traceability of digital objects. Focusing on art domain, this study ex- plores the operations and implications of NFT-based digital artwork markets through the viewpoint of artists and collectors. The first data were collected in 2021 from various insiders in the NFT community: the interviewees working and earning in this market segment are the most suitable profiles to delineate the structure of these activities; their responses were analyzed against the theo- retical framework that includes the notions of digital objects and blockchain technology, outlining NFT properties. The results were consistent, showing that blockchain technology can overcome the limitations of digital objects while opening up new challenges and possible risks

A Privacy-Preserving Dialogue System Based on Argumentation

Dialogue systems are a class of increasingly popular AI-based solutions to support timely and interactive communication with users in many domains. Due to the apparent possibility of users disclosing their sensitive data when interacting with such systems, ensuring that the systems follow the relevant laws, regulations, and ethical principles should be of primary concern. In this context, we discuss the main open points regarding these aspects and propose an approach grounded on a computational argumentation framework. Our approach ensures that user data are managed according to data minimization, purpose limitation, and integrity. Moreover, it is endowed with the capability of providing motivations for the system responses to offer transparency and explainability. We illustrate the architecture using as a case study a COVID-19 vaccine information system, discuss its theoretical properties, and evaluate it empirically

Multimodal Argument Mining: A Case Study in Political Debates

We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal

Contaminanti nelle acque lacustri e seston

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