On solving textual ambiguities and semantic vagueness in MRC based question answering using generative pre-trained transformers.

ABSTRACT: Machine reading comprehension (MRC) is one of the most challenging tasks and active fields in natural language processing (NLP). MRC systems aim to enable a machine to understand a given context in natural language and to answer a series of questions about it. With the advent of bi-directional deep learning algorithms and large-scale datasets, MRC achieved improved results. However, these models are still suffering from two research issues: textual ambiguities and semantic vagueness to comprehend the long passages and generate answers for abstractive MRC systems. To address these issues, this paper proposes a novel Extended Generative Pretrained Transformers-based Question Answering (ExtGPT-QA) model to generate precise and relevant answers to questions about a given context. The proposed

SUBMITTER: Ahmed M 

PROVIDER: S-EPMC10403182 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature