RoBERTa: a machine reading comprehension for climate change question answering in natural language processing

Abstract

With the advancement of artificial intelligence technology in various domains in the past few years, the question-answering system has brought important changes to the knowledge acquisition process. When compared to the conventional retrieval question-answering system, the question- answering system that uses machine reading comprehension can provide short and accurate answers. This thesis proposes an intelligent question-answering approach based on information retrieval and machine reading comprehension. To begin, a two-stage information retrieval approach is developed. To produce a contextualized vector representation, the first dense vector technique (SRoBERTa) is utilized to roughly collect relevant climate change material. Second, an algorithm (DPR) is used, which precisely uses and organizes related paragraphs in order to obtain replies to the paragraph that are incredibly relevant to the question at hand. The model is then improved using a mechanism known as RoBERTa during the machine reading comprehension stage. It is carried out by utilizing these texts and then looking for concise and to-the-point solutions. When compared to other common methods, the results of information retrieval and reading comprehension show that the models developed in this study perform well.