Gabriele Sarti


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A dissemination workshop for introducing young Italian students to NLP
Lucio Messina | Lucia Busso | Claudia Roberta Combei | Alessio Miaschi | Ludovica Pannitto | Gabriele Sarti | Malvina Nissim
Proceedings of the Fifth Workshop on Teaching NLP

We describe and make available the game-based material developed for a laboratory run at several Italian science festivals to popularize NLP among young students.

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Teaching NLP with Bracelets and Restaurant Menus: An Interactive Workshop for Italian Students
Ludovica Pannitto | Lucia Busso | Claudia Roberta Combei | Lucio Messina | Alessio Miaschi | Gabriele Sarti | Malvina Nissim
Proceedings of the Fifth Workshop on Teaching NLP

Although Natural Language Processing is at the core of many tools young people use in their everyday life, high school curricula (in Italy) do not include any computational linguistics education. This lack of exposure makes the use of such tools less responsible than it could be, and makes choosing computational linguistics as a university degree unlikely. To raise awareness, curiosity, and longer-term interest in young people, we have developed an interactive workshop designed to illustrate the basic principles of NLP and computational linguistics to high school Italian students aged between 13 and 18 years. The workshop takes the form of a game in which participants play the role of machines needing to solve some of the most common problems a computer faces in understanding language: from voice recognition to Markov chains to syntactic parsing. Participants are guided through the workshop with the help of instructors, who present the activities and explain core concepts from computational linguistics. The workshop was presented at numerous outlets in Italy between 2019 and 2020, both face-to-face and online.

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That Looks Hard: Characterizing Linguistic Complexity in Humans and Language Models
Gabriele Sarti | Dominique Brunato | Felice Dell’Orletta
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

This paper investigates the relationship between two complementary perspectives in the human assessment of sentence complexity and how they are modeled in a neural language model (NLM). The first perspective takes into account multiple online behavioral metrics obtained from eye-tracking recordings. The second one concerns the offline perception of complexity measured by explicit human judgments. Using a broad spectrum of linguistic features modeling lexical, morpho-syntactic, and syntactic properties of sentences, we perform a comprehensive analysis of linguistic phenomena associated with the two complexity viewpoints and report similarities and differences. We then show the effectiveness of linguistic features when explicitly leveraged by a regression model for predicting sentence complexity and compare its results with the ones obtained by a fine-tuned neural language model. We finally probe the NLM’s linguistic competence before and after fine-tuning, highlighting how linguistic information encoded in representations changes when the model learns to predict complexity.