Alessandro Mazzei

Also published as: A Mazzei


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Educational Dialogue Systems for Visually Impaired Students: Introducing a Task-Oriented User-Agent Corpus
Elisa Di Nuovo | Manuela Sanguinetti | Pier Felice Balestrucci | Luca Anselma | Cristian Bernareggi | Alessandro Mazzei
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

This paper describes a corpus consisting of real-world dialogues in English between users and a task-oriented conversational agent, with interactions revolving around the description of finite state automata. The creation of this corpus is part of a larger research project aimed at developing tools for an easier access to educational content, especially in STEM fields, for users with visual impairments. The development of this corpus was precisely motivated by the aim of providing a useful resource to support the design of such tools. The core feature of this corpus is that its creation involved both sighted and visually impaired participants, thus allowing for a greater diversity of perspectives and giving the opportunity to identify possible differences in the way the two groups of participants interacted with the agent. The paper introduces this corpus, giving an account of the process that led to its creation, i.e. the methodology followed to obtain the data, the annotation scheme adopted, and the analysis of the results. Finally, the paper reports the results of a classification experiment on the annotated corpus, and an additional experiment to assess the annotation capabilities of three large language models, in view of a further expansion of the corpus.

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Exploring Data Augmentation in Neural DRS-to-Text Generation
Muhammad Saad Amin | Luca Anselma | Alessandro Mazzei
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Neural networks are notoriously data-hungry. This represents an issue in cases where data are scarce such as in low-resource languages. Data augmentation is a technique commonly used in computer vision to provide neural networks with more data and increase their generalization power. When dealing with data augmentation for natural language, however, simple data augmentation techniques similar to the ones used in computer vision such as rotation and cropping cannot be employed because they would generate ungrammatical texts. Thus, data augmentation needs a specific design in the case of neural logic-to-text systems, especially for a structurally rich input format such as the ones used for meaning representation. This is the case of the neural natural language generation for Discourse Representation Structures (DRS-to-Text), where the logical nature of DRS needs a specific design of data augmentation. In this paper, we adopt a novel approach in DRS-to-Text to selectively augment a training set with new data by adding and varying two specific lexical categories, i.e. proper and common nouns. In particular, we propose using WordNet supersenses to produce new training sentences using both in-and-out-of-context nouns. We present a number of experiments for evaluating the role played by augmented lexical information. The experimental results prove the effectiveness of our approach for data augmentation in DRS-to-Text generation.


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Personalizing Weekly Diet Reports
Elena Monfroglio | Lucas Anselma | Alessandro Mazzei
Proceedings of the First Workshop on Natural Language Generation in Healthcare

In this paper we present the main components of a weekly diet report generator (DRG) in natural language. The idea is to produce a text that contains information on the adherence of the dishes eaten during a week to the Mediterranean diet. The system is based on a user model, a database of the dishes eaten during the week and on the automatic computation of the Mediterranean Diet Score. All these sources of information are exploited to produce a highly personalized text. The system has two main goals, related to two different kinds of users: on the one hand, when used by dietitians, the main goal is to highlight the most salient medical information of the patient diet and, on the other hand, when used by final users, the main goal is to educate them toward a Mediterranean style of eating.


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Content Selection for Explanation Requests in Customer-Care Domain
Luca Anselma | Mirko Di Lascio | Dario Mana | Alessandro Mazzei | Manuela Sanguinetti
2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence

This paper describes a content selection module for the generation of explanations in a dialogue system designed for customer care domain. First we describe the construction of a corpus of a dialogues containing explanation requests from customers to a virtual agent of a telco, and second we study and formalize the importance of a specific information content for the generated message. In particular, we adapt the notions of importance and relevance in the case of schematic knowledge bases.

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Annotating Errors and Emotions in Human-Chatbot Interactions in Italian
Manuela Sanguinetti | Alessandro Mazzei | Viviana Patti | Marco Scalerandi | Dario Mana | Rossana Simeoni
Proceedings of the 14th Linguistic Annotation Workshop

This paper describes a novel annotation scheme specifically designed for a customer-service context where written interactions take place between a given user and the chatbot of an Italian telecommunication company. More specifically, the scheme aims to detect and highlight two aspects: the presence of errors in the conversation on both sides (i.e. customer and chatbot) and the “emotional load” of the conversation. This can be inferred from the presence of emotions of some kind (especially negative ones) in the customer messages, and from the possible empathic responses provided by the agent. The dataset annotated according to this scheme is currently used to develop the prototype of a rule-based Natural Language Generation system aimed at improving the chatbot responses and the customer experience overall.


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Using NLG for speech synthesis of mathematical sentences
Alessandro Mazzei | Michele Monticone | Cristian Bernareggi
Proceedings of the 12th International Conference on Natural Language Generation

People with sight impairments can access to a mathematical expression by using its LaTeX source. However, this mechanisms have several drawbacks: (1) it assumes the knowledge of the LaTeX, (2) it is slow, since LaTeX is verbose and (3) it is error-prone since LATEX is a typographical language. In this paper we study the design of a natural language generation system for producing a mathematical sentence, i.e. a natural language sentence expressing the semantics of a mathematical expression. Moreover, we describe the main results of a first human based evaluation experiment of the system for Italian language.

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The DipInfoUniTo Realizer at SRST’19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization
Alessandro Mazzei | Valerio Basile
Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)

We describe the system presented at the SR’19 shared task by the DipInfoUnito team. Our approach is based on supervised machine learning. In particular, we divide the SR task into two independent subtasks, namely word order prediction and morphology inflection prediction. Two neural networks with different architectures run on the same input structure, each producing a partial output which is recombined in the final step in order to produce the predicted surface form. This work is a direct successor of the architecture presented at SR’19.


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PoSTWITA-UD: an Italian Twitter Treebank in Universal Dependencies
Manuela Sanguinetti | Cristina Bosco | Alberto Lavelli | Alessandro Mazzei | Oronzo Antonelli | Fabio Tamburini
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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The DipInfo-UniTo system for SRST 2018
Valerio Basile | Alessandro Mazzei
Proceedings of the First Workshop on Multilingual Surface Realisation

This paper describes the system developed by the DipInfo-UniTo team to participate to the shallow track of the Surface Realization Shared Task 2018. The system employs two separate neural networks with different architectures to predict the word ordering and the morphological inflection independently from each other. The UniTO realizer is language independent, and its simple architecture allowed it to be scored in the central part of the final ranking of the shared task.

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Designing and testing the messages produced by a virtual dietitian
Luca Anselma | Alessandro Mazzei
Proceedings of the 11th International Conference on Natural Language Generation

This paper presents a project about the automatic generation of persuasive messages in the context of the diet management. In the first part of the paper we introduce the basic mechanisms related to data interpretation and content selection for a numerical data-to-text generation architecture. In the second part of the paper we discuss a number of factors influencing the design of the messages. In particular, we consider the design of the aggregation procedure. Finally, we present the results of a human-based evaluation concerning this design factor.

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CheckYourMeal!: diet management with NLG
Luca Anselma | Simone Donetti | Alessandro Mazzei | Andrea Pirone
Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2IS&NLG)


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Annotating Italian Social Media Texts in Universal Dependencies
Manuela Sanguinetti | Cristina Bosco | Alessandro Mazzei | Alberto Lavelli | Fabio Tamburini
Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)


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Combinatorics vs Grammar: Archeology of Computational Poetry in Tape Mark I
Alessandro Mazzei | Andrea Valle
Proceedings of the INLG 2016 Workshop on Computational Creativity in Natural Language Generation

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SimpleNLG-IT: adapting SimpleNLG to Italian
Alessandro Mazzei | Cristina Battaglino | Cristina Bosco
Proceedings of the 9th International Natural Language Generation conference


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Translating Italian to LIS in the Rail Stations
Alessandro Mazzei
Proceedings of the 15th European Workshop on Natural Language Generation (ENLG)


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Sign Language Generation with Expert Systems and CCG
Alessandro Mazzei
INLG 2012 Proceedings of the Seventh International Natural Language Generation Conference


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An Ontology Based Architecture for Translation
Leonardo Lesmo | Alessandro Mazzei | Daniele P. Radicioni
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

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Building a Generator for Italian Sign Language
Alessandro Mazzei
Proceedings of the 13th European Workshop on Natural Language Generation


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Comparing the Influence of Different Treebank Annotations on Dependency Parsing
Cristina Bosco | Simonetta Montemagni | Alessandro Mazzei | Vincenzo Lombardo | Felice Dell’Orletta | Alessandro Lenci | Leonardo Lesmo | Giuseppe Attardi | Maria Simi | Alberto Lavelli | Johan Hall | Jens Nilsson | Joakim Nivre
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

As the interest of the NLP community grows to develop several treebanks also for languages other than English, we observe efforts towards evaluating the impact of different annotation strategies used to represent particular languages or with reference to particular tasks. This paper contributes to the debate on the influence of resources used for the training and development on the performance of parsing systems. It presents a comparative analysis of the results achieved by three different dependency parsers developed and tested with respect to two treebanks for the Italian language, namely TUT and ISST--TANL, which differ significantly at the level of both corpus composition and adopted dependency representations.


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Comparing Italian parsers on a common Treebank: the EVALITA experience
Cristina Bosco | Alessandro Mazzei | Vincenzo Lombardo | Giuseppe Attardi | Anna Corazza | Alberto Lavelli | Leonardo Lesmo | Giorgio Satta | Maria Simi
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The EVALITA 2007 Parsing Task has been the first contest among parsing systems for Italian. It is the first attempt to compare the approaches and the results of the existing parsing systems specific for this language using a common treebank annotated using both a dependency and a constituency-based format. The development data set for this parsing competition was taken from the Turin University Treebank, which is annotated both in dependency and constituency format. The evaluation metrics were those standardly applied in CoNLL and PARSEVAL. The results of the parsing results are very promising and higher than the state-of-the-art for dependency parsing of Italian. An analysis of such results is provided, which takes into account other experiences in treebank-driven parsing for Italian and for other Romance languages (in particular, the CoNLL X & 2007 shared tasks for dependency parsing). It focuses on the characteristics of data sets, i.e. type of annotation and size, parsing paradigms and approaches applied also to languages other than Italian.

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Evaluation of Natural Language Tools for Italian: EVALITA 2007
Bernardo Magnini | Amedeo Cappelli | Fabio Tamburini | Cristina Bosco | Alessandro Mazzei | Vincenzo Lombardo | Francesca Bertagna | Nicoletta Calzolari | Antonio Toral | Valentina Bartalesi Lenzi | Rachele Sprugnoli | Manuela Speranza
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

EVALITA 2007, the first edition of the initiative devoted to the evaluation of Natural Language Processing tools for Italian, provided a shared framework where participants’ systems had the possibility to be evaluated on five different tasks, namely Part of Speech Tagging (organised by the University of Bologna), Parsing (organised by the University of Torino), Word Sense Disambiguation (organised by CNR-ILC, Pisa), Temporal Expression Recognition and Normalization (organised by CELCT, Trento), and Named Entity Recognition (organised by FBK, Trento). We believe that the diffusion of shared tasks and shared evaluation practices is a crucial step towards the development of resources and tools for Natural Language Processing. Experiences of this kind, in fact, are a valuable contribution to the validation of existing models and data, allowing for consistent comparisons among approaches and among representation schemes. The good response obtained by EVALITA, both in the number of participants and in the quality of results, showed that pursuing such goals is feasible not only for English, but also for other languages.


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Multilingual Ontological Analysis of European Directives
Gianmaria Ajani | Guido Boella | Leonardo Lesmo | Alessandro Mazzei | Piercarlo Rossi
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions


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A Development Tool For Multilingual Ontology-based Conceptual
G. Ajani | G. Boella | L. Lesmo | M. Martin | A Mazzei | P. Rossi
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper introduces a number theoretical and practical issues related to the “Syllabus”. Syllabusis a multi-lingua ontology based tool, designed to improve the applications of the European Directives in the various European countries.


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Building a Large Grammar for Italian
Alessandro Mazzei | Vincenzo Lombardo
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Competence and Performance Grammar in Incremental Processing
Vincenzo Lombardo | Alessandro Mazzei | Patrick Sturt
Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together