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        <title>KU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI</title>
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    <abstract>In this paper, we describe our system and results submitted for the Natural Language Inference (NLI) track of the MEDIQA 2019 Shared Task. As KU_ai team, we used BERT as our baseline model and pre-processed the MedNLI dataset to mitigate the negative impact of de-identification artifacts. Moreover, we investigated different pre-training and transfer learning approaches to improve the performance. We show that pre-training the language model on rich biomedical corpora has a significant effect in teaching the model domain-specific language. In addition, training the model on large NLI datasets such as MultiNLI and SNLI helps in learning task-specific reasoning. Finally, we ensembled our highest-performing models, and achieved 84.7% accuracy on the unseen test dataset and ranked 10th out of 17 teams in the official results.</abstract>
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    <identifier type="doi">10.18653/v1/W19-5045</identifier>
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%0 Conference Proceedings
%T KU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI
%A Cengiz, Cemil
%A Sert, Ulaş
%A Yuret, Deniz
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F cengiz-etal-2019-ku
%X In this paper, we describe our system and results submitted for the Natural Language Inference (NLI) track of the MEDIQA 2019 Shared Task. As KU_ai team, we used BERT as our baseline model and pre-processed the MedNLI dataset to mitigate the negative impact of de-identification artifacts. Moreover, we investigated different pre-training and transfer learning approaches to improve the performance. We show that pre-training the language model on rich biomedical corpora has a significant effect in teaching the model domain-specific language. In addition, training the model on large NLI datasets such as MultiNLI and SNLI helps in learning task-specific reasoning. Finally, we ensembled our highest-performing models, and achieved 84.7% accuracy on the unseen test dataset and ranked 10th out of 17 teams in the official results.
%R 10.18653/v1/W19-5045
%U https://aclanthology.org/W19-5045
%U https://doi.org/10.18653/v1/W19-5045
%P 427-436

Markdown (Informal)

[KU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI](https://aclanthology.org/W19-5045) (Cengiz et al., BioNLP 2019)

• KU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI (Cengiz et al., BioNLP 2019)

ACL

• Cemil Cengiz, Ulaş Sert, and Deniz Yuret. 2019. KU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 427–436, Florence, Italy. Association for Computational Linguistics.