BioAMA: Towards an End to End BioMedical Question Answering System
Vasu Dev Sharma, Nitish Kulkarni, Srividya Pranavi, Gabriel Bayomi, Eric Nyberg, Teruko Mitamura · 2018
In this paper, we present a novel Biomedical Question Answering system, BioAMA: "Biomedical Ask Me Anything" on task 5b of the annual BioASQ challenge (Balikas et al., 2015).We focus on a wide variety of question types including factoid, list based, summary and yes/no type questions that generate both exact and wellformed 'ideal' answers.For summarytype questions, we combine effective IRbased techniques for retrieval and diversification of relevant snippets for a question to create an end-to-end system which achieves a ROUGE-2 score of 0.72 and a ROUGE-SU4 score of 0.71 on ideal answer questions (7% improvement over the previous best model).Additionally, we propose a novel Natural Language Inference (NLI) based framework to answer the yes/no questions.To train the NLI model, we also devise a transfer-learning technique by cross-domain projection of word embeddings.Finally, we present a two-stage approach to address the factoid and list type questions by first generating a candidate set using NER taggers and ranking them using both supervised and unsupervised techniques.