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- W4240156667 abstract "Information understanding and retrieval have al-ways been a challenge in natural language processing (NLP), especially in specific domains like the medical one. Natural language inference (NLI) is the task that focuses on under-standing the information using inference, while recognizing question entailment (RQE) is used for question answering systems, establishing if two questions can have the same answer. NLI and RQE have gained popularity in the medical field with the increased language model performance. However, the number of approaches is still limited. This study uses the state-of-the-art language model, BioMed-RoBERTa, to be fine-tuned for NLI and RQE tasks on medical data. We propose a method to automatically build a dataset for question entailment(QE) based on a generalized definition of QE according to which there is an entailment between two questions iff there is an entailment between their answers. The built dataset was used for fine-tuning a model which obtains good results on MEDIQA 2019 RQE task." @default.
- W4240156667 created "2022-05-12" @default.
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- W4240156667 date "2021-10-28" @default.
- W4240156667 modified "2023-10-18" @default.
- W4240156667 title "Medical Question Entailment based on Textual Inference and Fine-tuned BioMed-RoBERTa" @default.
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- W4240156667 doi "https://doi.org/10.1109/iccp53602.2021.9733687" @default.
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