  Despite the high accuracy offered by state-of-the-art deep natural-language
models (e.g. LSTM, BERT), their application in real-life settings is still
widely limited, as they behave like a black-box to the end-user. 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 done. However, robust and specialized xAI
(Explainable Artificial Intelligence) solutions tailored to deep
natural-language models are still missing. We propose a new framework, named
T-EBAnO, which provides innovative prediction-local and class-based
model-global explanation strategies tailored to black-box deep natural-language
models. Given a deep NLP model and the textual input data, T-EBAnO provides an
objective, human-readable, domain-specific assessment of the reasons behind the
automatic decision-making process. Specifically, the framework extracts sets of
interpretable features mining the inner knowledge of the model. Then, it
quantifies the influence of each feature during the prediction process by
exploiting the novel normalized Perturbation Influence Relation index at the
local level and the novel Global Absolute Influence and Global Relative
Influence indexes at the global level. The effectiveness and the quality of the
local and global explanations obtained with T-EBAnO are proved on (i) a
sentiment analysis task performed by a fine-tuned BERT model, and (ii) a toxic
comment classification task performed by an LSTM model.
