PubMedQA: A Dataset for Biomedical Research Question Answering
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Summary
The best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1% accuracy, compared to single human performance of 78.0% accuracy and majority-baseline of 55.2% accuracy.
- Type
- article
- Published
- 2019-09-01
- Cited by
- 1,872
- References
- 23
- Access
- Open access
- OpenAlex
- https://openalex.org/W2970482702
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:202572622
Keywords
Question answering, Computer science, Natural language processing, Natural (archaeology), Natural language
References
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- Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?
- The Stanford CoreNLP Natural Language Processing Toolkit
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- RACE: Large-scale ReAding Comprehension Dataset From Examinations
- Enhanced LSTM for Natural Language Inference
- The NarrativeQA Reading Comprehension Challenge
- A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis
- Deep Contextualized Word Representations
- Bag-of-Words as Target for Neural Machine Translation
- BioRead: A New Dataset for Biomedical Reading Comprehension
- HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
- emrQA: A Large Corpus for Question Answering on Electronic Medical Records
- Interpretation of Natural Language Rules in Conversational Machine Reading
- BioBERT: a pre-trained biomedical language representation model for biomedical text mining
- Natural Questions: A Benchmark for Question Answering Research
- BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- SQuAD: 100,000+ Questions for Machine Comprehension of Text
- Probing Biomedical Embeddings from Language Models
Cited by
- Generating Biomedical Question Answering Corpora From Q&A Forums
- A Gated Dilated Convolution with Attention Model for Clinical Cloze-Style Reading Comprehension
- Learning Contextualized Document Representations for Healthcare Answer Retrieval
- Prerequisites for Explainable Machine Reading Comprehension: A Position Paper
- Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset
- Fact or Fiction: Verifying Scientific Claims
- Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond
- Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models
- Bridging Hierarchical and Sequential Context Modeling for Question-driven Extractive Answer Summarization
- CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
- New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles
- Biomedical and Clinical English Model Packages in the Stanza Python NLP Library
- Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
- Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives
- E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce
- Unsupervised Pre-training for Biomedical Question Answering
- NCU-IISR: Using a Pre-trained Language Model and Logistic Regression Model for BioASQ Task 8b Phase B
- Overview of BioASQ 8a and 8b: Results of the Eighth Edition of the BioASQ Tasks a and b
- Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)
- Multi-hop Inference for Question-driven Summarization
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