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Hence, explainability is rapidly becoming a fundamental requirement of future-generation data-driven systems based on deep-learning approaches. Several attempts to fulfill the existing gap between accuracy and interpretability have been made. However, robust and specialized eXplainable Artificial Intelligence solutions, tailored to deep natural-language models, are still missing. We propose a new framework, named <jats:sc>T-EBAnO<\/jats:sc>, which provides innovative prediction-local and class-based model-global explanation strategies tailored to deep learning natural-language models. Given a deep NLP model and the textual input data, <jats:sc>T-EBAnO<\/jats:sc> provides an objective, human-readable, domain-specific assessment of the reasons behind the automatic decision-making process. Specifically, the framework extracts sets of <jats:italic>interpretable features<\/jats:italic> mining the inner knowledge of the model. Then, it quantifies the influence of each feature during the prediction process by exploiting the <jats:italic>normalized Perturbation Influence Relation<\/jats:italic> index at the local level and the novel <jats:italic>Global Absolute Influence<\/jats:italic> and <jats:italic>Global Relative Influence<\/jats:italic> indexes at the global level. The effectiveness and the quality of the local and global explanations obtained with <jats:sc>T-EBAnO<\/jats:sc> are proved on an extensive set of experiments addressing different tasks, such as a sentiment-analysis task performed by a fine-tuned BERT model and a toxic-comment classification task performed by an LSTM model. The quality of the explanations proposed by <jats:sc>T-EBAnO<\/jats:sc>, and, specifically, the correlation between the influence index and human judgment, has been evaluated by humans in a survey with more than 4000 judgments. To prove the generality of <jats:sc>T-EBAnO<\/jats:sc> and its model\/task-independent methodology, experiments with other models (ALBERT, ULMFit) on popular public datasets (Ag News and Cola) are also discussed in detail.<\/jats:p>","DOI":"10.1007\/s10115-022-01690-9","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T22:02:50Z","timestamp":1655935370000},"page":"1863-1907","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Trusting deep learning natural-language models via local and global explanations"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3398-8265","authenticated-orcid":false,"given":"Francesco","family":"Ventura","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7239-9602","authenticated-orcid":false,"given":"Salvatore","family":"Greco","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0538-9775","authenticated-orcid":false,"given":"Daniele","family":"Apiletti","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9039-6226","authenticated-orcid":false,"given":"Tania","family":"Cerquitelli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"key":"1690_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on explainable artificial intelligence (xai). 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