Semantically Corroborating Neural Attention for Biomedical Question Answering

Oita, Marilena; Vani, K.; Oezdemir-Zaech, Fatma

doi:10.1007/978-3-030-43887-6_60

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…In Table 7, we randomly sample such cases. Due to the lack of space, we provide more examples of cases at our url 5 . In here, we use the extractive QA setting to measure the upper-bound performance of our method.…”

Section: Discussionmentioning

confidence: 99%

“…we compare our results with the best results from last year's BioASQ Challenge Task 7B (Phase B)[5][6][7]14,29,36]. From this comparison, we observe that training BioBERT on the MNLI dataset significantly improves its performance on the Yes/No (+5.59%), Factoid (+0.53%), and List (+13.58%) type questions.…”

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confidence: 88%

“…Dimitriadis & Tsoumakas [14] 0.5541 --Hosein et al, [7] -0.4562 -Oita et al, [5] 0.4831 --Resta et al, [29] 0.7873 --Telukuntla et al, [6] 0.4486 0.4751 0.2002 Yoon et al, [36] 0…”

Section: Reference Systemunclassified

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Transferability of Natural Language Inference to Biomedical Question Answering

Jeong,

Sung,

Kim

et al. 2020

Preprint

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Biomedical question answering (QA) is a challenging task due to the scarcity of data and the requirement of domain expertise. Pre-trained language models have been used to address these issues. Recently, learning relationships between sentence pairs has been proved to improve performance in general QA. In this paper, we focus on applying BioBERT to transfer the knowledge of natural language inference (NLI) to biomedical QA. We observe that BioBERT trained on the NLI dataset obtains better performance on Yes/No (+5.59%), Factoid (+0.53%), List type (+13.58%) questions compared to performance obtained in a previous challenge (BioASQ 7B Phase B). We present a sequential transfer learning method that significantly performed well in the 8 th BioASQ Challenge (Phase B). In sequential transfer learning, the order in which tasks are fine-tuned is important. We measure an unanswerable rate of the extractive QA setting when the formats of factoid and list type questions are converted to the format of the Stanford Question Answering Dataset (SQuAD).

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Section: Discussionmentioning

confidence: 99%

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confidence: 88%

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Transferability of Natural Language Inference to Biomedical Question Answering

Jeong,

Sung,

Kim

et al. 2020

Preprint

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“…The "BIOASQ VK " systems were based on BioBERT [26], but with novel modifications to allow the model to cope with yes/no, factoid and list questions [41]. They pre-trained the model on the SQUAD dataset (for factoid and list questions) and SQUAD2 (for yes/no questions) to leverage the small size of the BioASQ dataset and by exploiting different pre-/post-processing techniques they obtained great results on all subtasks.…”

Section: Task 7bmentioning

confidence: 99%

Results of the seventh edition of the BioASQ Challenge

Nentidis,

Bougiatiotis,

Krithara

et al. 2020

Preprint

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The results of the seventh edition of the BioASQ challenge are presented in this paper. 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. In total, 30 teams with more than 100 systems participated in the challenge this year. As in previous years, the best systems were able to outperform the strong baselines. This suggests that stateof-the-art systems are continuously improving, pushing the frontier of research.

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