Italy

In this paper, we present BERT-POS, a simple method for encoding syntax into BERT embeddings without retraining or finetuning data, based on Part-Of-Speech (POS). Although fine-tuning is the most popular method to apply BERT models on domain datasets, it remains expensive in terms of training time, computing resources, training data selection and retraining frequency. Our alternative works at the preprocessing level and relies on POS tagging sentences. It gives interesting results for words similarity regarding out-of-vocabulary both in terms of domain-specific words and misspellings. More specifically, the experiments were done on French language, but we believe that they would be similar on others.

AIxIA 2021

Easy-to-use combination of POS and BERT model for domain-specific and misspelled terms

workshop paper

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The 20th International Conference of the Italian Association for Artificial Intelligence (**AIxIA 2021**) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

 
 



**General Chair**

Stefania Bandini, University of Milano-Bicocca, Italyand RCAST - The University of Tokyo, Japan


**Program Chairs**

Viviana Mascardi, University of Genova, Italy; Matteo Palmonari, University of Milano-Bicocca, Italy; Giuseppe Vizzari, University of Milano-Bicocca, Italy

**Organizing Committee**

Francesca Gasparini, University of Milano-Bicocca, Italy (**Chair**); Stefano Ferilli, University of Bari, Italy; Marta Giltri, University of Milano-Bicocca, Italy; Daniela Briola, University of Insubria, Italy


**Publicity and Communication Committee**

Elisabetta Fersini, University of Milano-Bicocca, Italy (**Chair**); Angelo Ferrando, University of Genova, Italy; Francesca Alessandra Lisi, University of Bari, Italy; Giovanni Stilo, University of L'Aquila, Italy; Marco Viviani, University of Milano-Bicocca, Italy


**Workshop Chairs**

Gabriella Cortellessa, ISTC-CNR, Rome, Italy; Silvia Rossi, University of Naples Federico II, Italy; Fabio Stella, University of Milano-Bicocca, Italy


**PC Members**

Fulvio Mastrogiovanni, University of Genova, Italy; Roberto Basili, University of Rome Tor Vergata, Italy; Nicola Gatti, Polytechnic University of Milan, Italy; Stefano Bistarelli, University of Perugia, Italy; Piero Poccianti, Consorzio Operativo Gruppo MPS, Italy; Francesco Scarcello, University of Calabria, Italy; Flavio S. Correa Da Silva, University of São Paulo, Brazil; Elena Cabrio, Université Côte d'Azur, CNRS, Inria, I3S, France; Antonio Lieto, University of Turin, Italy; Diego Calvanese, Free University of Bozen-Bolzano, Italy; Alessio Micheli, University of Pisa, Italy; Francesca A. Lisi University of Bari Aldo Moro, Italy; Mauro Vallati, University of Huddersfield, United Kingdom; Bernardo Magnini, Fondazione Bruno Kessler (FBK), Italy; Luigia C. Aiello, Sapienza University of Rome, Italy; Rafael H. Bordini, Pontifical Catholic University of Rio Grande do Sul, Brazil; Chiara Ghidini, Fondazione Bruno Kessler (FBK), Italy; Federico Bergenti, University of Parma, Italy; Francesco Ricca, University of Calabria, Italy; Andrea Burattin, Technical University of Denmark, Denmark; Angelo Oddi, ISTC-CNR, Italian National Research Council, Italy; Farshad Badie, Aalborg University, Denmark; Emanuel Sallinger, University of Oxford, United Kingdom; Marin Lujak, University Rey Juan Carlos, Spain; Giancarlo Fortino, University of Calabria, Italy; Salvatore Gaglio, University of Palermo, Italy; Johannes K. Fichte, TU Dresden, Germany; Matteo Baldoni, University of Turin, Italy; Fabrizio Riguzzi, Univeristy of Ferrara, Italy; Ivana Dusparic, Trinity College Dublin, Ireland; Serena Villata, CNRS - Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, France; Paolo Rosso, Technical University of Valencia, Spain; Ana L. C. Bazzan, Federal University of Rio Grande do Sul, Brazil; Gianluigi Greco, University of Calabria, Italy; Stefano Cagnoni, University of Parma, Italy; Gian Luca Pozzato, University of Turin, Italy; Salvatore Ruggieri, University of Turin, Italy; Claudio Di Ciccio, Sapienza University of Rome, Italy; Eloisa Vargiu, Cetaqua, Water Technology Center, Spain; Marco Maratea, University of Genova, Italy; Andrea Roli, University of Bologna, Italy; Viviana Patti, University of Turin, Italy; Stefania Costantini, University of L'Aquila, Italy; Antonius Weinzierl, Vienna University of Technology, Austria; Silvia Rossi, University of Naples Federico II, Italy; Giovanni Semeraro, University of Bari, Italy; Luciano Serafini, Fondazione Bruno Kessler (FBK), Italy; Laura Giordano, University of Eastern Piedmont, Italy; Ernesto Jimenez-Ruiz, City, University of London, United Kingdom; Marco Gavanelli, University of Ferrara, Italy; Federico Chesani, University of Bologna, Italy

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The 20th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2021) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

In this we paper present the last version of Tint, an opensource, fast and extendable Natural Language Processing suite for Italian based on Stanford CoreNLP. The new release includes a set of text processing components for fine-grained linguistic analysis, from tokenization to relation extraction, including part-of-speech tagging, morphological analysis, lemmatization, multi-word expression recognition, dependency parsing, named-entity recognition, keyword extraction, and much more. Tint is written in Java freely distributed under the GPL license. Although some modules do not perform at a state-of-the-art level, Tint reaches very good accuracy in all modules, and can be easily used outof-the-box.

Tint, the Swiss-Army Tool for Natural Language Processing in Italian

Multi-modal neural models that are able to encode and process both visual and textual data are becoming more and more common in the last few years. Such models enable new ways to learn the interaction between vision and text, and thus can be successfully applied to tasks of varying complexity in the domain of image and text classification. However, such models are traditionally oriented to learn grounded properties of images and of the objects they depict and less suited to solve tasks involving subjective characteristics, such as the emotions they can convey in viewers. In this paper, we provide some insights in the performances of the recently released OpenAI CLIP model for an emotion classification task. We evaluate the model both under zero-shot settings and via fine tuning on an image-emotion dataset. We compare the performances of CLIP both in a zero-shot and fine-tuning setting on (i) a standard benchmark dataset for object recognition (ii) an image-emotion dataset. Moreover, we evaluate to which extent a CLIP model adapted to emotions is able to retain general knowledge and generalization capabilities

Leveraging CLIP for Image Emotion Recognition

Dialogue systems are AI applications widely used in many contexts requiring user interaction. However, unconstrained interaction may lead to users communicating sensitive data. This raises concerns about how these systems handle personal data, and about their compliance with relevant laws, regulations, and ethical principles. We propose to integrate advanced natural language processing techniques in a dialogue system architecture based on computational argumentation, ensuring that user data are ethically managed and regulations are respected. A preliminary experimental evaluation of our proposal over a COVID-19 vaccine information case study shows promising results.

A Preliminary Evaluation of a Privacy-Preserving Dialogue System

This paper presents recent developments of a software system that acts as an artificial player for a popular word association game. The game was proposed for the Evaluation Campaign of Natural Language Processing and Speech Tools for Italian in 2020, and it attracted the interest of various researchers. Several aspects of the recent developments of the artificial player are discussed, from the collection of the texts used to acquire sufficient linguistic knowledge, to the improvements of the algorithm employed to play the game. Preliminary, but encouraging, experimental results are also discussed in comparison with other artificial players for the same game.

Preliminary Experiments on an Improved Artificial Player for a Word Association Game

In this paper, we propose an evaluation of a Transformerbased punctuation restoration model for the Italian language. Experimenting with a BERT-base model, we perform several fine-tuning with different training data and sizes and tested them in an in- and crossdomain scenario. Moreover, we offer a comparison in a multilingual setting with the same model fine-tuned on English transcriptions. Finally, we conclude with an error analysis of the main weaknesses of the model related to specific punctuation marks

Evaluating Transformer Models for Punctuation Restoration in Italian

In this paper, we present MT-GAN-BERT, i.e., a BERT-based architecture for faceted classification tasks. It aims to reduce the requirements of Transformers both in terms of the amount of annotated data and the computational cost required at classification time. First, MT-GAN-BERT enables semi-supervised learning in BERT-based architectures based on Generative Adversarial Learning. Second, it implements a Multi-task Learning approach to solve multiple tasks simultaneously. A single BERTbased model is used to encode the input examples, while multiple linear layers are used to implement the classification steps, with a significant reduction of the computational costs. Experimental evaluations against six classification tasks involved in detecting abusive languages in Italian suggest that MT-GAN-BERT represents a sustainable solution that generally improves the raw adoption of multiple BERT-based models with lighter requirements in terms of annotated data and computational costs.

MT-GAN-BERT Multi-Task and Generative Adversarial Learning for sustainable Language Processing

Neuromorphic computing by spatial light modulation in photonic systems

A central tenet of explainable AI is that the bugs and biases affecting a model can
be uncovered by computing and analyzing explanations for the model’s predictions.
However, and crucially, techniques for explaining machine learning models do not
enable experts to correct the bugs that they expose. In this talk, I will overview
recent work on debugging machine learning models that approach the problem by
supplying corrective supervision on the model’s explanations. In particular, I will
discuss approaches based on local attribute-based explanations, global explanations,
as well as example-based explanations. Moreover, I will illustrate how these techniques
can be generalized to concept-based models by mixing attribute- and concept-level
supervision. I will conclude by outlining some important open issues in this flourishing
research topic.

Beyond Extracting Explanations

Language is a social construct. We use it to achieve various conversational goals. Only one among them is to convey information. However, natural language processing has traditionally focused only on this informational aspect, ignoring all social aspects of language. That restriction was partially necessary to make modeling progress. However, I argue that as modeling power increases, we might want to revisit the issue. Social aspects of language can help disambiguate meaning, add more nuance to our models, and are becoming increasingly important in all aspects of generation. In this talk, I will outline several of the social dimensions that influence language use, how they affect NLP models, and what efforts are already underway to incorporate them. I will conclude with some open questions and ideas for future directions. If we manage to include social aspects of language into NLP, I believe we will open new research avenues, improve performance, and create fairer language technology.

More than words – Integrating social factors into language modeling

Disagreement in annotation, traditionally treated mostly as noise, is now more and more often considered as a source of valuable information instead. We investigate a particular form of disagreement, occurring when the focus of an annotated dataset is a subjective and controversial phenomenon, therefore inducing a certain degree of polarization among the annotators’ judgments. We argue that the polarization is indicative of the conflicting perspectives held by different annotator groups, and propose a quantitative method to model this phenomenon. Moreover, we introduce a method to automatically identify shared perspectives stemming from a common background. We test our method on several corpora in English and Italian, manually annotated according to their hate speech content, validating prior knowledge about the groups of annotators, when available, and discovering characteristic traits among annotators with unknown background. We found several precisely defined perspectives, described in terms of increased sensitivity towards textual content expressing attitudes such as xenophobia, islamophobia, and homophobia.

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