ARS NITK at MEDIQA 2019: Analysing various methods for natural language inference, recognising question entailment and medical question answering system

Abstract

This paper includes approaches we have taken for Natural Language Inference, Question Entailment Recognition and Question-Answering tasks to improve domain-specific Information Retrieval. Natural Language Inference (NLI) is a task that aims to determine if a given hypothesis is an entailment, contradiction or is neutral to the given premise. Recognizing Question Entailment (RQE) focuses on identifying entailment between two questions while the objective of Question-Answering (QA) is to filter and improve the ranking of automatically retrieved answers. For addressing the NLI task, the UMLS Metathesaurus was used to find the synonyms of medical terms in given sentences, on which the InferSent model was trained to predict if the given sentence is an entailment, contradictory or neutral. We also introduce a new Extreme gradient boosting model built on PubMed embeddings to perform RQE. Further, a closed-domain Question Answering technique that uses Bi-directional LSTMs trained on the SquAD dataset to determine relevant ranks of answers for a given question is also discussed. Experimental validation showed that the proposed models achieved promising results. © 2019 Association for Computational Linguistics