Invited Talks

From Whispers to Structures: Computational Argumentation in Politics

Serena Villata

Argument(ation) Mining (AM) is the dimension of computational argumentation aiming at automatically processing natural language arguments and reason upon them. More precisely, argument mining aims at extracting, classifying and analysing natural language arguments and their relations from text, with the final goal of providing machine-processable structured data for computational models of argument. In this keynote talk, I will first introduce this research area, highlight-ing the main successful tasks and open issues in identifying argumentative structures from different kinds of texts (e.g., clinical trials, online user generated content, news articles). Then, I will present a key challenge which conjugate argument mining with formal computational models of argumentation, i.e., the assessment of the trustworthiness of natural language arguments, with a focus on fallacious argumentation, with the aim to show how these methods can be used to automatically identify fallacious arguments in political debates. I will conclude with some thoughts on the challenge of automatic generation of counter-arguments to fight online disinformation and hate speech.

Can Requirements Make Machine Learning Safer? From constrained prediction to structured generation

Eleonora  Giunchiglia

Abstract: As machine learning systems move from narrow prediction tasks to generative and decision-making settings, their ability to satisfy domain requirements is becoming increasingly important. Yet deep neural networks can still produce outputs that violate even simple logical, structural, or regulatory constraints. This talk argues that requirements should be treated as first-class citizens in machine learning: not only as criteria for evaluating a model after deployment, but as objects that can shape what models learn, generate, and optimize for from the outset. I will show how logical requirements can define safe output spaces and be compiled into neural architectures, enabling predictions and generations that are compliant by design. Through examples in autonomous driving, tabular data generation, and neurosymbolic reasoning, the talk will illustrate how requirements can make learning systems safer, more data-efficient, and better aligned with the structure of the problems they are meant to solve.

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