IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering
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Summary
This paper presents the experiments accomplished as a part of the participation in the MEDIQA challenge, an (Abacha et al., 2019) shared task, and results yield encouraging results in all the three tasks.
- Type
- article
- Published
- 2019-06-14
- Cited by
- 5
- References
- 12
- Access
- Open access
- OpenAlex
- https://openalex.org/W2950367548
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:189928301
Keywords
Question answering, Natural language, Logical consequence, Inference, Natural (archaeology)
References
- Software Framework for Topic Modelling with Large Corpora
- Long Short-Term Memory
- Methods for Using Textual Entailment in Open-Domain Question Answering
- Siamese Recurrent Architectures for Learning Sentence Similarity
- Overview of the Medical Question Answering Task at TREC 2017 LiveQA
- Lessons from Natural Language Inference in the Clinical Domain
- BioBERT: a pre-trained biomedical language representation model for biomedical text mining
- A question-entailment approach to question answering
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
- The Probabilistic Relevance Framework: BM25 and Beyond
Cited by
- Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
- TAQS: An Arabic Question Similarity System Using Transfer Learning of BERT With BiLSTM
- Research on Dual-Dimensional Entity Association-Based Question and Answering Technology for Smart Medicine
- ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems
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