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Akademický článek
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Publikováno v:
Proceedings of the 3rd Workshop on Perspectivist Approaches to NLP (NLPerspectives) @ LREC-COLING 2024
This paper explores the correlation between linguistic diversity, sentiment analysis and transformer model architectures. We aim to investigate how different English variations impact transformer-based models for irony detection. To conduct our study
Externí odkaz:
http://arxiv.org/abs/2406.02338
Publikováno v:
Il Foro Italiano, 2006 Sep 01. 129(9), 2399/2400-2403/2404.
Externí odkaz:
https://www.jstor.org/stable/23201638
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 from limitations such as architecture specificity, excessive complexity
Externí odkaz:
http://arxiv.org/abs/2402.03142
Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languages
Autor:
Ruzzetti, Elena Sofia, Ranaldi, Federico, Logozzo, Felicia, Mastromattei, Michele, Ranaldi, Leonardo, Zanzotto, Fabio Massimo
Publikováno v:
Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, 2023, pages 14447 - 14461
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 novel standpoint to investigate the above issue: using typological si
Externí odkaz:
http://arxiv.org/abs/2305.02215
Publikováno v:
Il Foro Italiano, 1974 Jan 01. 97, 1293/1294-1295/1296.
Externí odkaz:
https://www.jstor.org/stable/23165299
Publikováno v:
Il Foro Italiano, 1964 Jan 01. 87(7), 271/272-271/272.
Externí odkaz:
https://www.jstor.org/stable/23154566
Autor:
Xompero, Giancarlo A., Mastromattei, Michele, Salman, Samir, Giannone, Cristina, Favalli, Andrea, Romagnoli, Raniero, Zanzotto, Fabio Massimo
Publikováno v:
Proceedings of the Language Resources and Evaluation Conference, 2022
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, we investigate how the use of explicit domain knowledge of conversati
Externí odkaz:
http://arxiv.org/abs/2109.13029
Autor:
Ruzzetti, Elena Sofia, Ranaldi, Leonardo, Mastromattei, Michele, Fallucchi, Francesca, Zanzotto, Fabio Massimo
Publikováno v:
Findings of the Association for Computational Linguistics: ACL 2022
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 covered by traditional dictionaries. In this paper, we propose to use
Externí odkaz:
http://arxiv.org/abs/2109.11763
Akademický článek
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