Implementation of interpretable methods for paraphrasing and text disambiguation

Abstract

In this project, starting from an interpretable language model based on knowledge graphs, four essential methods for natural language processing (NLP) have been developed, namely (i) paraphrasing, (ii) part-of-speech tagging, (iii) semantic similarity analysis, and (iv) text simplification. The methods yield good results on a small dataset and thus offer promising prospects for continuing research on interpretable NLP. Applications of NLP are becoming increasingly embedded in our daily lives in applications such as voice assistants, automatic language translation, opinion mining and medical diagnostics. One of the reasons behind the exponentially growing interest in NLP is the development of deep neural network (DNN) models that have achieved outstanding performance on various NLP tasks. However, the domination of DNN models has been followed by deep concerns regarding the black-box nature of such systems. By contrast, the language model used here is fully interpretable, paving the way for safe and accountable NLP.