Understanding stance classification of BERT models: an attention-based framework

Author:

Sáenz Carlos Abel Córdova,Becker Karin

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Reference37 articles.

1. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems (NIPS’17), pp 6000–6010

2. Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, (NAACL-HLT), pp 4171–4186

3. Tenney I, Das D, Pavlick E (2019) BERT rediscovers the classical NLP pipeline. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 4593–4601. https://doi.org/10.18653/v1/P19-1452

4. Rogers A, Kovaleva O, Rumshisky A (2020) A primer in bertology: What we know about how bert works. Trans Assoc Comput Linguist 8:842–866. https://doi.org/10.1162/tacl_a_00349

5. Ventura F, Greco S, Apiletti D, Cerquitelli T (2022) Trusting deep learning natural-language models via local and global explanations. Knowl Inf Syst 64(7):1863–1907. https://doi.org/10.1007/s10115-022-01690-9

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