Question Classification using Interpretable Tsetlin Machine

Question Answering (QA) is one of the hottest research topics in Natural Language Processing (NLP) as well as Information Retrieval (IR). Among various domains of QA, Question Classification (QC) is a very important system that classifies a question based on the type of answer expected from it. Generalization is a very important factor in NLP specifically in QA and subsequently in QC. There are numerousmodels for the classification of types of questions. Despite its good performance, it lacks the interpretability that shows us how the model can classify the output. Hence, in this paper, we propose a Tsetlin Machine based QC task that shows the interpretability of the model yet retaining the state-of-the-art performance. Our model is validated by comparing it with other interpretable machine learning algorithms.