Paper Info
Title
MSIT_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain
Abstract
In this paper, we present Biomedical Multi-Task Deep Neural Network (Bio-MTDNN) on the NLI task of MediQA 2019 challenge (Ben Abacha et al., 2019). Bio-MTDNN utilizes "transfer learning" based paradigm where not only the source and target domains are different but also the source and target tasks are varied, although related. Further, Bio-MTDNN integrates knowledge from external sources such as clinical databases (UMLS) enhancing its performance on the clinical domain. Our proposed method outperformed the official baseline and other prior models (such as ESIM and Infersent on dev set) by a considerable margin as evident from our experimental results.
Year
DOI
Venue
2019
10.18653/v1/w19-5052
SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2019)
Field
DocType
Citations 
Computer science,Natural language processing,Artificial intelligence,Natural language inference
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Sahil Chopra100.34
Ankita Gupta244.18
Anupama Kaushik3152.56