SAGE: Semantic-Aware Global Explanations for Named Entity Recognition

In the last decades, deep learning approaches achieved impressive results in many research fields, such as Computer Vision and Natural Language Processing (NLP). NLP in particular has greatly benefit from unsupervised methods that allow to learn distributed representation of language. On the race for better performances Language Models have reached hundred of billions parameters nowadays. Despite the remarkable results, deep models are still far from being fully exploited in real world applications. Indeed, these approaches are black-boxes, i.e. they are not interpretable by design nor explainable, which is often crucial to make decisions in business. Several task-agnostic methods have been proposed in literature to explain models' decisions. Most techniques rely on the “local” assumption, i.e. explanations are made example-wise. In this paper instead, we present a post-hoc method to produce highly interpretable global rules to explain NLP classifiers. Rules are extracted with a data mining approach on a semantically enriched input representation, instead of using words/wordpieces solely. Semantic information yields more abstract and general rules that are both more explanatory and less complex, while being also better at reflecting the model behaviour. In the experiments we focus on Named Entity Recognition, an NLP task where explainability is under-investigated. We explain the predictions of BERT NER classifiers trained on two popular benchmarks, CoNLL03 and Ontonotes, and compare our model against LIME [1] and Decision Trees.