Built on

Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th annual meeting of the association for computational linguistics. pp. 384–394. Association for Computational Linguistics (2010)
2010
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Pyysalo, S., Ginter, F., Moen, H., Salakoski, T., Ananiadou, S.: Distributional semantics resources for biomedical text processing. Proceedings of LBM pp. 39–44 (2013)
2013
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Peng, S., Zhang, Y., You, R., Xie, Z., Wang, B., Zhu, S.: The fudan participation in the 2015 bioasq challenge: Large-scale biomedical semantic indexing and question answering. In: CEUR Workshop Proceedings. vol. 1391. CEUR Workshop Proceedings (2015)
2015
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Tsatsaronis, G., Balikas, G., Malakasiotis, P., Partalas, I., Zschunke, M., Alvers, M.R., Weissenborn, D., Krithara, A., Petridis, S., Polychronopoulos, D., et al.: An overview of the bioasq large-scale biomedical semantic indexing and question answering competition. BMC bioinformatics 16
2015
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Krithara, A., Nentidis, A., Paliouras, G., Kakadiaris, I.: Results of the 4th edition of BioASQ challenge. In: Proceedings of the Fourth BioASQ workshop. pp. 1–7. Association for Computational Linguistics, Berlin, Germany (Aug 2016). https://doi.org/10.18653/v1/W16-3101, https://www.aclweb.org/anthology/W16-3101
2016
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Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)
Original
2016
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Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al.: Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
Original
2016
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Yang, Z., Zhou, Y., Nyberg, E.: Learning to answer biomedical questions: Oaqa at bioasq 4b. In: Proceedings of the Fourth BioASQ workshop. pp. 23–37 (2016)
2016
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Nentidis, A., Bougiatiotis, K., Krithara, A., Paliouras, G., Kakadiaris, I.: Results of the fifth edition of the bioasq challenge. In: BioNLP 2017. pp. 48–57 (2017)
2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
Earlier work this paper cites.

Similar

Wiese, G., Weissenborn, D., Neves, M.: Neural domain adaptation for biomedical question answering. arXiv preprint arXiv:1706.03610 (2017)
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Alsentzer, E., Murphy, J.R., Boag, W., Weng, W.H., Jin, D., Naumann, T., McDermott, M.: Publicly available clinical bert embeddings. arXiv preprint arXiv:1904.03323 (2019)
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2019
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Beltagy, I., Cohan, A., Lo, K.: Scibert: Pretrained contextualized embeddings for scientific text. arXiv preprint arXiv:1903.10676 (2019)
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BioASQ Participants Area BioASQ (May, 2019), http://participants-area.bioasq.org/results/7b/phaseB/
2019
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Dimitriadis, D., Tsoumakas, G.: Word embeddings and external resources for answer processing in biomedical factoid question answering. Journal of biomedical informatics 92
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2019

Pre-trained Language Model for Biomedical Question Answering

Yoon, Wonjin, Lee, Jinhyuk, Kim, Donghyeon et al.

Understand

The recent success of question answering systems is largely attributed to pre-trained language models.

However, as language models are mostly pre-trained on general domain corpora such as Wikipedia, they often have difficulty in understanding biomedical questions.
In this paper, we investigate the performance of BioBERT, a pre-trained biomedical language model, in answering biomedical questions including factoid, list, and yes/no type questions.
BioBERT uses almost the same structure across various question types and achieved the best performance in the 7th BioASQ Challenge (Task 7b, Phase B).

Built on

Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th annual meeting of the association for computational linguistics. pp. 384–394. Association for Computational Linguistics (2010)
2010
Earlier work this paper cites.
Pyysalo, S., Ginter, F., Moen, H., Salakoski, T., Ananiadou, S.: Distributional semantics resources for biomedical text processing. Proceedings of LBM pp. 39–44 (2013)
2013
Earlier work this paper cites.
Peng, S., Zhang, Y., You, R., Xie, Z., Wang, B., Zhu, S.: The fudan participation in the 2015 bioasq challenge: Large-scale biomedical semantic indexing and question answering. In: CEUR Workshop Proceedings. vol. 1391. CEUR Workshop Proceedings (2015)
2015
Earlier work this paper cites.
Tsatsaronis, G., Balikas, G., Malakasiotis, P., Partalas, I., Zschunke, M., Alvers, M.R., Weissenborn, D., Krithara, A., Petridis, S., Polychronopoulos, D., et al.: An overview of the bioasq large-scale biomedical semantic indexing and question answering competition. BMC bioinformatics 16
2015
Earlier work this paper cites.
Krithara, A., Nentidis, A., Paliouras, G., Kakadiaris, I.: Results of the 4th edition of BioASQ challenge. In: Proceedings of the Fourth BioASQ workshop. pp. 1–7. Association for Computational Linguistics, Berlin, Germany (Aug 2016). https://doi.org/10.18653/v1/W16-3101, https://www.aclweb.org/anthology/W16-3101
2016
Earlier work this paper cites.
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)
Original
2016
Earlier work this paper cites.
Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al.: Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
Original
2016
Earlier work this paper cites.
Yang, Z., Zhou, Y., Nyberg, E.: Learning to answer biomedical questions: Oaqa at bioasq 4b. In: Proceedings of the Fourth BioASQ workshop. pp. 23–37 (2016)
2016
Earlier work this paper cites.
Nentidis, A., Bougiatiotis, K., Krithara, A., Paliouras, G., Kakadiaris, I.: Results of the fifth edition of the bioasq challenge. In: BioNLP 2017. pp. 48–57 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
Earlier work this paper cites.

Similar

Wiese, G., Weissenborn, D., Neves, M.: Neural domain adaptation for biomedical question answering. arXiv preprint arXiv:1706.03610 (2017)
Original
2017
Cited alongside, same era.
Wiese, G., Weissenborn, D., Neves, M.: Neural question answering at bioasq 5b. arXiv preprint arXiv:1706.08568 (2017)
Original
2017
Cited alongside, same era.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Original
2018
Cited alongside, same era.
Lim, S., Kang, J.: Chemical–gene relation extraction using recursive neural network. Database 2018
2018
Cited alongside, same era.

Then

Alsentzer, E., Murphy, J.R., Boag, W., Weng, W.H., Jin, D., Naumann, T., McDermott, M.: Publicly available clinical bert embeddings. arXiv preprint arXiv:1904.03323 (2019)
Original
2019
Closest in time.
Beltagy, I., Cohan, A., Lo, K.: Scibert: Pretrained contextualized embeddings for scientific text. arXiv preprint arXiv:1903.10676 (2019)
Original
2019
Closest in time.
BioASQ Participants Area BioASQ (May, 2019), http://participants-area.bioasq.org/results/7b/phaseB/
2019
Closest in time.
Dimitriadis, D., Tsoumakas, G.: Word embeddings and external resources for answer processing in biomedical factoid question answering. Journal of biomedical informatics 92
2019
Closest in time.
Kim, D., Lee, J., So, C.H., Jeon, H., Jeong, M., Choi, Y., Yoon, W., Sung, M., Kang, J.: A neural named entity recognition and multi-type normalization tool for biomedical text mining. IEEE Access 7

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Nentidis, A., Krithara, A., Bougiatiotis, K., Paliouras, G., Kakadiaris, I.: Results of the sixth edition of the BioASQ challenge. In: Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering. pp. 1–10. Association for Computational Linguistics, Brussels, Belgium (Nov 2018), https://www.aclweb.org/anthology/W18-5301

2018

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Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). pp. 2227–2237 (2018)

2018

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Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding with unsupervised learning. Tech. rep., Technical report, OpenAI (2018)

2018

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Rajpurkar, P., Jia, R., Liang, P.: Know what you don’t know: Unanswerable questions for squad. arXiv preprint arXiv:1806.03822 (2018)

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2018

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Rosso-Mateus, A., González, F.A., Montes-y Gómez, M.: Mindlab neural network approach at bioasq 6b. In: Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering. pp. 40–46 (2018)

2018

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Zhu, H., Paschalidis, I.C., Tahmasebi, A.: Clinical concept extraction with contextual word embedding. arXiv preprint arXiv:1810.10566 (2018)

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2019

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Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., Kang, J.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics (09 2019). https://doi.org/10.1093/bioinformatics/btz682

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Sousa, D., Lamurias, A., Couto, F.M.: Using neural networks for relation extraction from biomedical literature. arXiv preprint arXiv:1905.11391 (2019)

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Talmor, A., Berant, J.: Multiqa: An empirical investigation of generalization and transfer in reading comprehension. arXiv preprint arXiv:1905.13453 (2019)

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Yoon, W., So, C.H., Lee, J., Kang, J.: Collabonet: collaboration of deep neural networks for biomedical named entity recognition. BMC bioinformatics 20

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