10 citations · 14 across the 5 of their papers we have counts for
5 papers
Linguistic Fingerprint in Transformer Models: How Language Variation Influences Parameter Selection in Irony Detection
Michele Mastromattei, Fabio Massimo Zanzotto
This paper explores the correlation between linguistic diversity, sentiment analysis and transformer model architectures. We aim to investigate how different English variations imp…
Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language Models
Michele Mastromattei, Fabio Massimo Zanzotto
Neural network pruning has become increasingly crucial due to the complexity of these models and their widespread use in various fields. Existing pruning algorithms often suffer fr…
Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languages
Elena Sofia Ruzzetti, Federico Ranaldi, Felicia Logozzo +3
The impressive achievements of transformers force NLP researchers to delve into how these models represent the underlying structure of natural language. In this paper, we propose a…
Every time I fire a conversational designer, the performance of the dialog system goes down
Giancarlo A. Xompero, Michele Mastromattei, Samir Salman +4
Incorporating explicit domain knowledge into neural-based task-oriented dialogue systems is an effective way to reduce the need of large sets of annotated dialogues. In this paper,…
Lacking the embedding of a word? Look it up into a traditional dictionary
Elena Sofia Ruzzetti, Leonardo Ranaldi, Michele Mastromattei +2
Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often…