Multilingual multi-aspect explainability analyses on machine reading comprehension models [PDF]
iScience, 2022 Summary: Achieving human-level performance on some of the machine reading comprehension (MRC) datasets is no longer challenging with the help of powerful pre-trained language models (PLMs).
Yiming Cui +5 more
doaj +3 more sources
On solving textual ambiguities and semantic vagueness in MRC based question answering using generative pre-trained transformers [PDF]
PeerJ Computer Science, 2023 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 ...
Muzamil Ahmed +5 more
doaj +3 more sources
BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task [PDF]
Bioinformatics, 2022 ABSTRACTMotivationBiomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets.
Maria Mahbub +3 more
openaire +4 more sources
A Multiple-Choice Machine Reading Comprehension Model with Multi-Granularity Semantic Reasoning
Applied Sciences, 2021 To address the problem of poor semantic reasoning of models in multiple-choice Chinese machine reading comprehension (MRC), this paper proposes an MRC model incorporating multi-granularity semantic reasoning.
Yu Dai, Yufan Fu, Lei Yang
doaj +2 more sources
Information Extraction Network Based on Multi-Granularity Attention and Multi-Scale Self-Learning [PDF]
Sensors, 2023 Transforming the task of information extraction into a machine reading comprehension (MRC) framework has shown promising results. The MRC model takes the context and query as the inputs to the encoder, and the decoder extracts one or more text spans as ...
Weiwei Sun +4 more
doaj +2 more sources
Neural Processing Letters, 2022 Financial named entity recognition (FinNER) from literature is a challenging task in the field of financial text information extraction, which aims to extract a large amount of financial knowledge from unstructured texts.
Zhang, Yuzhe, Zhang, Hong
core +2 more sources
ExpMRC: explainability evaluation for machine reading comprehension [PDF]
Heliyon, 2022 Achieving human-level performance on some Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).
Yiming Cui +4 more
doaj +2 more sources
Multigranularity Syntax Guidance with Graph Structure for Machine Reading Comprehension
Applied Sciences, 2022 In recent years, pre-trained language models, represented by the bidirectional encoder representations from transformers (BERT) model, have achieved remarkable success in machine reading comprehension (MRC).
Chuanyun Xu +4 more
doaj +2 more sources
Resolving passage ambiguity in machine reading comprehension using lightweight transformer architectures [PDF]
Scientific ReportsMachine Reading Comprehension (MRC) refers to generating precise responses to the users’ queries from text content using natural language processing. The exponential growth and complexities of online content have made it difficult to surf the required ...
Adnan Nawaz +5 more
doaj +2 more sources
IEEE Access, 2023 Machine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP), which focuses on teaching machines to read and understand the meaning of texts based on the emergence of large-scale datasets and neural network models.
Tuan-Anh Phan +2 more
doaj +2 more sources