Papers with biomedical natural language processing

3 papers
Meta-Learning Adaptive Knowledge Distillation for Efficient Biomedical Natural Language Processing (2022.findings-aacl)

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Challenge: Existing knowledge distillation methods have been proposed to reduce the size of large models for biomedical natural language processing tasks.
Approach: They propose a meta-learning approach which adaptively learns parameters that enable optimal rate of knowledge exchange between teacher and student models from the distillation data during knowledge distillation.
Outcome: The proposed method improves the performance of knowledge distillation methods on two biomedical natural language processing tasks.
BioReader: a Retrieval-Enhanced Text-to-Text Transformer for Biomedical Literature (2022.emnlp-main)

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Challenge: Recent research has equipped language models with the ability to attend over relevant and factual information from non-parametric external sources, drawing a complementary path to architectural scaling.
Approach: They propose a retrieval-enhanced text-to-text model that augments the input prompt by fetching and assembling relevant scientific literature chunks from a neural database centered on PubMed.
Outcome: The proposed model outperforms state-of-the-art models on a broad array of downstream tasks while using up to 3x fewer parameters.
Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization (2023.emnlp-main)

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Challenge: Existing methods for biomedical named entity recognition require laborious human effort.
Approach: They propose a Synonym Generalization framework that recognizes biomedical concepts using span-based predictions.
Outcome: The proposed framework outperforms dictionary-based approaches on a wide range of benchmarks.

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