6 research outputs found
Medical Knowledge-enriched Textual Entailment Framework
One of the cardinal tasks in achieving robust medical question answering systems is textual entailment. The existing approaches make use of an ensemble of pre-trained language models or data augmentation, often to clock higher numbers on the validation metrics. However, two major shortcomings impede higher success in identifying entailment: (1) understanding the focus/intent of the question and (2) ability to utilize the real-world background knowledge to capture the context beyond the sentence. In this paper, we present a novel Medical Knowledge-Enriched Textual Entailment framework that allows the model to acquire a semantic and global representation of the input medical text with the help of a relevant domain-specific knowledge graph. We evaluate our framework on the benchmark MEDIQA-RQE dataset and manifest that the use of knowledge enriched dual-encoding mechanism help in achieving an absolute improvement of 8.27% over SOTA language models. We have made the source code available here
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model
While deep learning techniques have shown promising results in many natural
language processing (NLP) tasks, it has not been widely applied to the clinical
domain. The lack of large datasets and the pervasive use of domain-specific
language (i.e. abbreviations and acronyms) in the clinical domain causes slower
progress in NLP tasks than that of the general NLP tasks. To fill this gap, we
employ word/subword-level based models that adopt large-scale data-driven
methods such as pre-trained language models and transfer learning in analyzing
text for the clinical domain. Empirical results demonstrate the superiority of
the proposed methods by achieving 90.6% accuracy in medical domain natural
language inference task. Furthermore, we inspect the independent strengths of
the proposed approaches in quantitative and qualitative manners. This analysis
will help researchers to select necessary components in building models for the
medical domain.Comment: 9 pages, Accepted to ACL 2019 workshop on BioNL