In this paper, we explore the impact of augmenting pre-trained Encoder-Decoder models, specifically T5, with linguistic knowledge for the prediction of a target task. In particular, we investigate whether fine-tuning a T5 model on an intermediate …
Mobile app reviews are a large-scale data source for software-related knowledge generation activities, including software maintenance, evolution and feedback analysis. Effective extraction of features (i.e., functionalities or characteristics) from …
In this paper, we present an evaluation of the influence of label selection on the performance of a Sequence-to-Sequence Transformer model in a classification task. Our study investigates whether the choice of words used to represent classification …
Traditional genre-based approaches for book recommendations face challenges due to the vague definition of genres. To overcome this, we propose a novel task called Book Author Prediction, where we predict the author of a book based on user-generated …
Language Learning Development (LangLearn) is the EVALITA 2023 shared task on automatic language development assessment, which consists in predicting the evolution of the written language abilities of learners across time. LangLearn is conceived to be …
Purpose The authors’ goal is to investigate variations in the writing style of book reviews published on different social reading platforms and referring to books of different genres, which enables acquiring insights into communication strategies …
The outstanding performance recently reached by neural language models (NLMs) across many natural language processing (NLP) tasks has steered the debate towards understanding whether NLMs implicitly learn linguistic competence. Probes, i.e., …
In this paper, we propose a comprehensive linguistic study aimed at assessing the implicit behaviour of one of the most prominent Neural Language Model (NLM) based on Transformer architectures, BERT (Devlin et al., 2019), when dealing with a …
In this paper, we propose an extensive evaluation of the first text-to-text Italian Neural Language Model (NLM), IT5, on a classification scenario. In particular, we test the performance of IT5 on several tasks involving both the classification of …
In this paper, we present an in-depth investigation of the linguistic knowledge encoded by the transformer models currently available for the Italian language. In particular, we investigate how the complexity of two different architectures of probing …