We propose a predictive ensemble model to classify idioms and literals using BERT and RoBERTa, fine-tuned with the TroFi dataset. Pre-trained deep learning models have been used for several text classification tasks though models like BERT and RoBERTa have not been exclusively used for idiom and literal classification. This paper deals with idiom identification as a text classification task. A fundamental NLP task is text classification, which categorizes text into structured categories known as text labeling or categorization. Automatic detection of Idioms plays an important role in all these applications. Detecting idioms automatically is a serious challenge in natural language processing (NLP) domain applications like information retrieval (IR), machine translation and chatbot. An idiom is a common phrase that means something other than its literal meaning.
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