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Neural Question Answering at BioASQ 5B
2017
BioNLP 2017
Preserved Fulltext
At the core of our system, we use FastQA, a state-ofthe-art neural QA system. ...
This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). ...
Conclusion In this paper, we summarized the system design of our BioASQ 5B submission for factoid and list questions. We use a neural architecture which is trained end-to-end on the QA task. ...
doi:10.18653/v1/w17-2309
dblp:conf/bionlp/WieseWN17
fatcat:k2qbakbyfva5ddsjnkj75v4vt4
Neural Question Answering at BioASQ 5B
[article]
2017
arXiv
pre-print
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At the core of our system, we use FastQA, a state-of-the-art neural QA system. ...
This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). ...
Conclusion In this paper, we summarized the system design of our BioASQ 5B submission for factoid and list questions. We use a neural architecture which is trained end-to-end on the QA task. ...
arXiv:1706.08568v1
fatcat:unzporpn6zg7nn46ig3e2t572e
Macquarie University at BioASQ 5b – Query-based Summarisation Techniques for Selecting the Ideal Answers
2017
BioNLP 2017
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Macquarie University's contribution to the BioASQ challenge (Task 5b Phase B) focused on the use of query-based extractive summarisation techniques for the generation of the ideal answers. ...
Our experiments and the ROUGE results of the five test batches of BioASQ indicate surprisingly good results for the trivial approach. ...
Conclusions At the time of writing, only the ROUGE scores of BioASQ 5b were available. ...
doi:10.18653/v1/w17-2308
dblp:conf/bionlp/Aliod17
fatcat:teia6uzribcshmhfuioszcigca
BioAMA: Towards an End to End BioMedical Question Answering System
2018
Proceedings of the BioNLP 2018 workshop
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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) . ...
0.72 and a ROUGE-SU4 score of 0.71 on ideal answer questions (7% improvement over the previous best model). ...
We achieve state of the art results in automatic evaluation measures for the ideal answer questions in Task 5b of the BioASQ dataset, yielding a 7% improvement over the previous state of the art system ...
doi:10.18653/v1/w18-2312
dblp:conf/bionlp/SharmaKPBNM18
fatcat:dyqv3lgsyvexzcfikxt4emsf2i
Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers
[article]
2017
arXiv
pre-print
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Macquarie University's contribution to the BioASQ challenge (Task 5b Phase B) focused on the use of query-based extractive summarisation techniques for the generation of the ideal answers. ...
Our experiments and the ROUGE results of the five test batches of BioASQ indicate surprisingly good results for the trivial approach. ...
-Phase B: Question answering. • BioASQ 5c: Funding information extraction from biomedical literature. The questions used in BioASQ 5b were of three types: yes/no, factoid, list, and summary. ...
arXiv:1706.02095v2
fatcat:orw6s35izndexgjms2gfvndnie
Measuring Domain Portability and ErrorPropagation in Biomedical QA
[article]
2019
arXiv
pre-print
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In this work we present Google's submission to the BioASQ 7 biomedical question answering (QA) task (specifically Task 7b, Phase B). ...
However, the biggest quality bottleneck is at the retrieval stage, where we see large drops in metrics – over 10pts absolute – when using non gold inputs to the QA model. ...
Table 1 . 1 Table showing the differences between the input at test time for each model.
Table 3 . 3 Performance on BioASQ Task 4 and 5b, Phase B averaged over all batches. ...
arXiv:1909.09704v2
fatcat:36n2tglfevevjhnbgwtvia2oli
How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering
[article]
2019
arXiv
pre-print
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We compare two pre-training models based on reading comprehension and open domain question answering models and determine the performance when fine-tuned and tested over BIOASQ question answering dataset ...
Similarly for question answering, pre-training and fine-tuning can be done in several ways. ...
In Question Answering (QA) specifically on open domain, several neural network models have been introduced, such as Convolutional Neural Networks(CNN), Recurrent Neural Networks(RNN) using GRUs or LSTMs ...
arXiv:1911.00712v1
fatcat:vsf5ur22ovbvvovvog55hq4lpq
Results of the fifth edition of the BioASQ Challenge
2017
BioNLP 2017
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The fifth challenge consisted of three tasks: semantic indexing, question answering and a new task on information extraction. ...
The goal of the BioASQ challenge is to engage researchers into creating cuttingedge biomedical information systems. ...
BioASQ is grateful to NLM for providing baselines for tasks 5a and 5c and the CMU team for providing the baselines for task 5b. Finally, we would also like to thank all teams for their participation. ...
doi:10.18653/v1/w17-2306
dblp:conf/bionlp/NentidisBKPK17
fatcat:zj42lfjjdvgvzcxarhbhx3la4y
Assessing the performance of Olelo, a real-time biomedical question answering application
2017
BioNLP 2017
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Question answering (QA) can support physicians and biomedical researchers to find answers to their questions in the scientific literature. ...
In addition to the BioASQ evaluation, we compared our system to other on-line biomedical QA systems in terms of the response time and the quality of the answers. 1 http://hpi.de/plattner/olelo sults for ...
task 5b phases B (exact answers). ...
doi:10.18653/v1/w17-2344
dblp:conf/bionlp/NevesEFU17
fatcat:lerk6gynazallo6vamr4coqubq
Bio-AnswerFinder: a system to find answers to questions from biomedical texts
2019
Database: The Journal of Biological Databases and Curation
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The answer sentences are further ranked by a fine-tuned bidirectional encoder representation from transformers (BERT) classifier trained using 100 answer candidate sentences per question for 492 BioASQ ...
Our unsupervised baseline system achieves a mean reciprocal rank score of 0.46 and Precision@1 of 0.32 on 936 questions from BioASQ. ...
(8) introduced a neural system to detect answer spans in the gold standard snippets provided by BioASQ for question answering task (Task B) competition. ...
doi:10.1093/database/baz137
pmid:31925435
pmcid:PMC7053013
fatcat:scxluxhtd5burcx2xkebte6die
Simple and Effective Semi-Supervised Question Answering
2018
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
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Recent success of deep learning models for the task of extractive Question Answering (QA) is hinged on the availability of large annotated corpora. ...
Our system exploits the document structure to create cloze-style questions from these base documents; pre-trains a powerful neural network on the cloze style questions; and further finetunes the model ...
We also test on the BioASQ 5b dataset, which consists of question-answer pairs from PubMed abstracts. ...
doi:10.18653/v1/n18-2092
dblp:conf/naacl/DhingraPR18
fatcat:ba7chq5x3jc37pypo53nzbezka
Simple and Effective Semi-Supervised Question Answering
[article]
2018
arXiv
pre-print
Preserved Fulltext
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Recent success of deep learning models for the task of extractive Question Answering (QA) is hinged on the availability of large annotated corpora. ...
Our system exploits the document structure to create cloze-style questions from these base documents; pre-trains a powerful neural network on the cloze style questions; and further fine-tunes the model ...
We also test on the BioASQ 5b dataset, which consists of question-answer pairs from PubMed abstracts. ...
arXiv:1804.00720v1
fatcat:byoni2t5zbcklmrepj7iwsz3ru
Biomedical Question Answering: A Survey of Approaches and Challenges
[article]
2021
arXiv
pre-print
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Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. ...
There have been tremendous developments of BQA in the past two decades, which we classify into 5 distinctive approaches: classic, information retrieval, machine reading comprehension, knowledge base and question ...
At BioASQ 5B, Mollá [117] observes that a trivial baseline that returns the top retrieved snippets as the ideal answer is hard to beat. ...
arXiv:2102.05281v2
fatcat:gngt6kffszbzbc3donik55dd64
Results of the Seventh Edition of the BioASQ Challenge
[chapter]
2020
Communications in Computer and Information Science
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2020 pre-print arXiv:2006.09174v1 fatcat:xr7danogirdznbw7obb3ygg3h4
The aim of the BioASQ challenge is the promotion of systems and methodologies through the organization of a challenge on the tasks of large-scale biomedical semantic indexing and question answering. ...
The results of the seventh edition of the BioASQ challenge are presented in this paper. ...
Acknowledgments Google was a proud sponsor of the BioASQ Challenge in 2018. The seventh edition of BioASQ is also sponsored by the Atypon Systems inc. ...
doi:10.1007/978-3-030-43887-6_51
fatcat:76ind5ijizdphjmcnzgk7fjmii
SciFive: a text-to-text transformer model for biomedical literature
[article]
2021
arXiv
pre-print
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BERT, BioBERT, Base T5) on tasks in named entity relation, relation extraction, natural language inference, and question-answering. ...
We test SciFive on the factoid questions from the BioASQ 4b, 5b, and 6b challenges Tsatsaronis et al. (2015) . To preprocess the BioASQ data, we use the same approach as Lee et al. (2019) . ...
For the
BioASQ tasks
Table 3 . 3 Expert assessment result on Question Answering tasks (Lenient
Accuracy)
Task
BioBERT T5
SciFive
(PubMed+PMC)
SciFive
(PMC)
SciFive
(Pubmed)
BioAsq 4b ...
arXiv:2106.03598v1
fatcat:vce5ki2sone3nab4jz2gp3moci
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