BioAMA: Towards an End to End BioMedical Question Answering System
Vasu Sharma, Nitish Kulkarni, Srividya Pranavi, Gabriel Bayomi, Eric Nyberg, Teruko Mitamura
Abstract
In this paper, we present a novel Biomedical Question Answering system, BioAMA: “Biomedical Ask Me Anything” on task 5b of the annual BioASQ challenge. In this work, we focus on a wide variety of question types including factoid, list based, summary and yes/no type questions that generate both exact and well-formed ‘ideal’ answers. For summary-type questions, we combine effective IR-based 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 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 or unsupervised techniques.- Anthology ID:
- W18-2312
- Volume:
- Proceedings of the BioNLP 2018 workshop
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 109–117
- Language:
- URL:
- https://aclanthology.org/W18-2312
- DOI:
- 10.18653/v1/W18-2312
- Cite (ACL):
- Vasu Sharma, Nitish Kulkarni, Srividya Pranavi, Gabriel Bayomi, Eric Nyberg, and Teruko Mitamura. 2018. BioAMA: Towards an End to End BioMedical Question Answering System. In Proceedings of the BioNLP 2018 workshop, pages 109–117, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- BioAMA: Towards an End to End BioMedical Question Answering System (Sharma et al., BioNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W18-2312.pdf
- Data
- SNLI