Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment
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Summary
This paper presents a multi-task learning approach to natural language inference and question entailment in the biomedical domain and shows that leveraging information from parallel tasks across domains along with medical knowledge integration allows the model to learn better biomedical feature representations.
- Type
- article
- Published
- 2019-08-01
- Cited by
- 8
- References
- 27
- Access
- Open access
- OpenAlex
- https://openalex.org/W2970344931
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:198972215
Keywords
Computer science, Task (project management), Inference, Artificial intelligence, Natural language processing
References
- A Simple Algorithm for Identifying Abbreviation Definitions in Biomedical Text
- An overview of MetaMap: historical perspective and recent advances
- The SIDER database of drugs and side effects
- The Unified Medical Language System (UMLS): integrating biomedical terminology
- Decomposing Consumer Health Questions
- Enriching Word Vectors with Subword Information
- Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference
- A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
- Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
- Recognizing Question Entailment for Medical Question Answering
- DrugBank 5.0: a major update to the DrugBank database for 2018
- Stochastic Answer Networks for Machine Reading Comprehension
- Construction of the Literature Graph in Semantic Scholar
- Lessons from Natural Language Inference in the Clinical Domain
- BioSentVec: creating sentence embeddings for biomedical texts
- BioBERT: a pre-trained biomedical language representation model for biomedical text mining
- A question-entailment approach to question answering
- ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
- SciBERT: Pretrained Contextualized Embeddings for Scientific Text
- GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Cited by
- Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model
- Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
- Medical Knowledge-enriched Textual Entailment Framework
- A survey on textual entailment based question answering
- Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study
- Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction
- Artificial intelligence: Machine learning approach for screening large database and drug discovery
- Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study (Preprint)
- Recognizing Question Entailment in Consumer Health Using a Query Formulation Approach
- Applying Natural Language Inference or Question Entailment for Crowdsourcing More Data
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