Adaptations of perturbation-based explanation methods for text classification with neural networksKlemen, Matej (Avtor) Robnik Šikonja, Marko (Mentor) perturbation-based explanation methodsdependency-based explanationstext generationIME explanationLIME explanationDeep neural networks are successfully used for text classification tasks. However, as their functioning is opaque to users, they may learn spurious patterns so we need mechanisms to explain their predictions. Current machine learning explanation methods are designed for general prediction and commonly assume tabular data. They mostly work by perturbing the inputs and assigning credit to the features that strongly impact the outputs. In our work, we propose modified versions of two popular explanation methods (Interactions-based Method for Explanation - IME, and Local Interpretable Model-agnostic Explanation - LIME) for explaining text classifiers. The methods generate input perturbations considering the input dependence. For that purpose, they use language models as generators of more natural perturbations. We first perform a distribution detection experiment, through which we empirically show that the generated perturbations are more natural than the perturbations used in the original IME and LIME. Then, we evaluate the quality of the computed explanations using automated metrics and compare them to the explanations calculated with the original methods. We find that their quality is generally worse, which we attribute to the generation strategy and metrics that measure a different type of importance. As a second contribution, we propose the calculation of IME and LIME explanations in terms of units, longer than words, by using the dependency structure to guide the process of grouping words. The custom explanations mostly decrease the redundancy in the explanations and can serve as a diagnostic tool for the behaviour of models.20212021-09-10 11:45:12Magistrsko delo/naloga130145sl