Text-to-text generative approach for enhanced complex word identification

This paper presents a novel approach for solving the Complex Word Identification (CWI) task using the text-to-text generative model. The CWI task involves identifying complex words in text, which is a challenging Natural Language Processing task. To our knowledge, it is a first attempt to address CWI problem into text-to-text context. In this work, we propose a new methodology that leverages the power of the Transformer model to evaluate complexity of words in binary and probabilistic settings. We also propose a novel CWI dataset, which consists of 62,200 phrases, both complex and simple. We train and fine-tune our proposed model on our CWI dataset. We also evaluate its performance on separate test sets across three different domains. Our experimental results demonstrate the effectiveness of our proposed approach compared to state-of-the-art methods. • The paper proposes a transformer based generative approach for complex word identification (CWI) • Our technique uses text generation and addresses the CWI task in the text-to-text context. • The paper also proposes and develops a new CWI dataset for solving the CWI task using the proposed method. • We fine-tune our model for CWI task in binary settings and it performs on par with state-of-the-art methods. • In addition, we also fine-tune the model using probabilistic settings and it achieves state-of-the-art results. • Our dataset and code is publicly available for the research community.