Abstract

Text classification is one of the primary NLP tasks where machine learning (ML) is widely used. Even though the applied machine learning models are similar, the classification task may address specific challenges from language to language. The concept of model explainability can provide an idea of how the models make decisions in these situations. In this paper, The explainability of different text classification models for Malayalam language, a morphologically rich Dravidian language predominantly spoken in Kerala, was compared. The experiments considered classification models from both traditional ML and deep learning genres. The experiments were conducted on three different datasets and explainability scores are formulated for each of the selected models. The results of experiments showed that deep learning models did very well with respect to performance matrices whereas traditional machine learning models did well if not better in the explainability part.