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Roberto Basili

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6 papers

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AAAI Conference 2020 Conference Paper

Actionable Ethics through Neural Learning

Daniele Rossini
Danilo Croce
Sara Mancini
Massimo Pellegrino
Roberto Basili

While AI is going to produce a great impact on society, its alignment with human values and expectations is an essential step towards a correct harnessing of AI potentials for good. There is a corresponding growing need for mature and established technical standards to enable the assessment of an AI application as the evaluation of its graded adherence to formalized ethics. This is clearly dependent on methods to inject ethical awareness at all stages of an AI application development and use. For this reason we introduce the notion of Embedding Principles of ethics by Design (EPbD) as a comprehensive inductive framework. Although extending generic AI applications, it mainly aims at learning the ethical behaviour through numerical optimization, i. e. deep neural models. The core idea is to support ethics by integrating automated reasoning over formal knowledge and induction from ethically enriched training data. A deep neural network is proposed here to model both the functional as well as the ethical conditions characterizing a target decision. In this way, the discovery of latent ethical knowledge is enabled and made available to the learning process. The application of the above framework to a banking application, i. e. AI-driven Digital Lending, is used to show how accurate classiﬁcation can be achieved without neglecting the ethical dimension. Results over existing datasets demonstrate that the ethical compliance of the sources can be used to output models able to optimally ﬁne tune the balance between business and ethical accuracy.

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AIJ Journal 2020 Journal Article

Grounded language interpretation of robotic commands through structured learning

Andrea Vanzo
Danilo Croce
Emanuele Bastianelli
Roberto Basili
Daniele Nardi

The presence of robots in everyday life is increasing day by day at a growing pace. Industrial and working environments, health-care assistance in public or domestic areas can benefit from robots' services to accomplish manifold tasks that are difficult and annoying for humans. In such scenarios, Natural Language interactions, enabling collaboration and robot control, are meant to be situated, in the sense that both the user and the robot access and make reference to the environment. Contextual knowledge may thus play a key role in solving inherent ambiguities of grounded language as, for example, the prepositional phrase attachment. In this work, we present a linguistic pipeline for semantic processing of robotic commands, that combines discriminative structured learning, distributional semantics and contextual evidence extracted from the working environment. The final goal is to make the interpretation process of linguistic exchanges depending on physical, cognitive and language-dependent aspects. We present, formalize and discuss an adaptive Spoken Language Understanding chain for robotic commands, that explicitly depends on the operational context during both the learning and processing stages. The resulting framework allows to model heterogeneous information concerning the environment (e. g. , positional information about the objects and their properties) and to inject it in the learning process. Empirical results demonstrate a significant contribution of such additional dimensions, achieving up to a 25% of relative error reduction with respect to a pipeline that only exploits linguistic evidence.

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JMLR Journal 2018 Journal Article

KELP: a Kernel-based Learning Platform

Simone Filice
Giuseppe Castellucci
Giovanni Da San Martino
Alessandro Moschitti
Danilo Croce
Roberto Basili

KELP is a Java framework that enables fast and easy implementation of kernel functions over discrete data, such as strings, trees or graphs and their combination with standard vectorial kernels. Additionally, it provides several kernel- based algorithms, e.g., online and batch kernel machines for classification, regression and clustering, and a Java environment for easy implementation of new algorithms. KELP is a versatile toolkit, very appealing both to experts and practitioners of machine learning and Java language programming, who can find extensive documentation, tutorials and examples of increasing complexity on the accompanying website. Interestingly, KELP can be also used without any knowledge of Java programming through command line tools and JSON/XML interfaces enabling the declaration and instantiation of articulated learning models using simple templates. Finally, the extensive use of modularity and interfaces in KELP enables developers to easily extend it with their own kernels and algorithms. [abs] [ pdf ][ bib ] [ code ] [ webpage ] &copy JMLR 2018. ( edit, beta )

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IJCAI Conference 2016 Conference Paper

A Discriminative Approach to Grounded Spoken Language Understanding in Interactive Robotics

Emanuele Bastianelli
Danilo Croce
Andrea Vanzo
Roberto Basili
Daniele Nardi

Spoken Language Understanding in Interactive Robotics provides computational models of human-machine communication based on the vocal input. However, robots operate in specific environments and the correct interpretation of the spoken sentences depends on the physical, cognitive and linguistic aspects triggered by the operational environment. Grounded language pro- cessing should exploit both the physical constraints of the context as well as knowledge assumptions of the robot. These include the subjective perception of the environment that explicitly affects linguistic reasoning. In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics. Empirical results achieve up to a 40% of relative error reduction.

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AAAI Conference 2015 Conference Paper

A Stratified Strategy for Efficient Kernel-Based Learning

Simone Filice
Danilo Croce
Roberto Basili

In Kernel-based Learning the targeted phenomenon is summarized by a set of explanatory examples derived from the training set. When the model size grows with the complexity of the task, such approaches are so computationally demanding that the adoption of comprehensive models is not always viable. In this paper, a general framework aimed at minimizing this problem is proposed: multiple classifiers are stratified and dynamically invoked according to increasing levels of complexity corresponding to incrementally more expressive representation spaces. Computationally expensive inferences are thus adopted only when the classification at lower levels is too uncertain over an individual instance. The application of complex functions is thus avoided where possible, with a significant reduction of the overall costs. The proposed strategy has been integrated within two well-known algorithms: Support Vector Machines and Passive-Aggressive Online classifier. A significant cost reduction (up to 90%), with a negligible performance drop, is observed against two Natural Language Processing tasks, i. e. Question Classification and Sentiment Analysis in Twitter.

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AIJ Journal 1996 Journal Article

An empirical symbolic approach to natural language processing

Roberto Basili
Maria Teresa Pazienza
Paola Velardi

Empirical methods in the field of natural language processing (NLP) are usually based on a probabilistic model of language. These methods recently gained popularity because of the claim that they provide a better coverage of language phenomena. Though this claim is not entirely proved, empirical methods certainly outperform in this regard rationalist, or symbolic, methods. However, empirical methods provide a probabilistic, not conceptual, explanation of the analyzed linguistic phenomena. Probabilistic systems do “work” in real applications, and this is meritorious, but in our view they are intrinsically unable to provide insight into the mechanisms of human communication, because the output is represented by plain words, or word clusters, with attached probabilities. Eventually, a human analyst must make sense of these data. In the past few years, we explored the possibility of combining the advantages of empirical and rationalist approaches in NLP. Our objective was to define methods for lexical knowledge acquisition that are both scalable and linguistically “appealing”, that is, amenable to a theoretically founded analysis of language. In this paper we describe and evaluate the results of a large-scale lexical learning system, ARIOSTO_LEX, that uses a combination of probabilistic and knowledge-based methods for the acquisition of selectional restrictions of words in sublanguages. We present many experimental data obtained from different corpora in different domains and languages, and show that the acquired lexical data not only have practical applications in NLP, but they are indeed useful for a comparative analysis of sublanguages. Importantly, ARIOSTO_LEX shed light on recurrent linguistic phenomena that have a problematic impact on the large-scale applicability of commonly used NLP techniques.

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