Intelligent Systems with Applications, 2023 Deep neural networks, despite their remarkable success in various language understanding tasks, have been found vulnerable to adversarial attacks and subtle input perturbations, revealing a robustness shortfall.
Jingliang Fang +5 more
doaj +4 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
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.
Mahbub M +3 more
europepmc +4 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
Integrate Candidate Answer Extraction with Re-Ranking for Chinese Machine Reading Comprehension [PDF]
Entropy, 2021 Machine Reading Comprehension (MRC) research concerns how to endow machines with the ability to understand given passages and answer questions, which is a challenging problem in the field of natural language processing.
Junjie Zeng +3 more
doaj +2 more sources
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 +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
Neural Processing Letters, 2023 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. It is widely accepted to use sequence tagging frameworks to implement FinNER tasks.
Yuzhe Zhang 0002, Hong Zhang
exaly +3 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
Application of machine reading comprehension techniques for named entity recognition in materials science [PDF]
Journal of CheminformaticsMaterials science is an interdisciplinary field that studies the properties, structures, and behaviors of different materials. A large amount of scientific literature contains rich knowledge in the field of materials science, but manually analyzing these
Zihui Huang +8 more
doaj +2 more sources