1 code implementation • NAACL (CMCL) 2021 • Gabriele Sarti, Dominique Brunato, Felice Dell’Orletta
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.
no code implementations • NAACL (CMCL) 2021 • Benedetta Iavarone, Dominique Brunato, Felice Dell’Orletta
We study the influence of context on how humans evaluate the complexity of a sentence in English.
no code implementations • SemEval (NAACL) 2022 • Roberto Zamparelli, Shammur Chowdhury, Dominique Brunato, Cristiano Chesi, Felice Dell’Orletta, Md. Arid Hasan, Giulia Venturi
We report the results of the SemEval 2022 Task 3, PreTENS, on evaluation the acceptability of simple sentences containing constructions whose two arguments are presupposed to be or not to be in an ordered taxonomic relation.
no code implementations • NAACL (DeeLIO) 2021 • Alessio Miaschi, Dominique Brunato, Felice Dell’Orletta, Giulia Venturi
This paper presents an investigation aimed at studying how the linguistic structure of a sentence affects the perplexity of two of the most popular Neural Language Models (NLMs), BERT and GPT-2.
no code implementations • COLING 2020 • Alessio Miaschi, Dominique Brunato, Felice Dell'Orletta, Giulia Venturi
In this paper we investigate the linguistic knowledge learned by a Neural Language Model (NLM) before and after a fine-tuning process and how this knowledge affects its predictions during several classification problems.
no code implementations • WS 2020 • Alessio Miaschi, Sam Davidson, Dominique Brunato, Felice Dell{'}Orletta, Kenji Sagae, Claudia Helena Sanchez-Gutierrez, Giulia Venturi
In this paper we present an NLP-based approach for tracking the evolution of written language competence in L2 Spanish learners using a wide range of linguistic features automatically extracted from students{'} written productions.
no code implementations • LREC 2020 • Dominique Brunato, Andrea Cimino, Felice Dell{'}Orletta, Giulia Venturi, Simonetta Montemagni
In this paper, we introduce Profiling{--}UD, a new text analysis tool inspired to the principles of linguistic profiling that can support language variation research from different perspectives.
no code implementations • EMNLP 2018 • Dominique Brunato, Lorenzo De Mattei, Felice Dell{'}Orletta, Benedetta Iavarone, Giulia Venturi
In this paper, we present a crowdsourcing-based approach to model the human perception of sentence complexity.