@inproceedings{bhaskar-etal-2019-sieg,
title = "Sieg at {MEDIQA} 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment",
author = "Bhaskar, Sai Abishek and
Rungta, Rashi and
Route, James and
Nyberg, Eric and
Mitamura, Teruko",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5049",
doi = "10.18653/v1/W19-5049",
pages = "462--470",
abstract = "This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain. Recognizing textual inference relations and question similarity can address the issue of answering new consumer health questions by mapping them to Frequently Asked Questions on reputed websites like the NIH. We show that leveraging information from parallel tasks across domains along with medical knowledge integration allows our model to learn better biomedical feature representations. Our final models for the NLI and RQE tasks achieve the 4th and 2nd rank on the shared-task leaderboard respectively.",
}
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<abstract>This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain. Recognizing textual inference relations and question similarity can address the issue of answering new consumer health questions by mapping them to Frequently Asked Questions on reputed websites like the NIH. We show that leveraging information from parallel tasks across domains along with medical knowledge integration allows our model to learn better biomedical feature representations. Our final models for the NLI and RQE tasks achieve the 4th and 2nd rank on the shared-task leaderboard respectively.</abstract>
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%0 Conference Proceedings
%T Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment
%A Bhaskar, Sai Abishek
%A Rungta, Rashi
%A Route, James
%A Nyberg, Eric
%A Mitamura, Teruko
%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 bhaskar-etal-2019-sieg
%X This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain. Recognizing textual inference relations and question similarity can address the issue of answering new consumer health questions by mapping them to Frequently Asked Questions on reputed websites like the NIH. We show that leveraging information from parallel tasks across domains along with medical knowledge integration allows our model to learn better biomedical feature representations. Our final models for the NLI and RQE tasks achieve the 4th and 2nd rank on the shared-task leaderboard respectively.
%R 10.18653/v1/W19-5049
%U https://aclanthology.org/W19-5049
%U https://doi.org/10.18653/v1/W19-5049
%P 462-470
Markdown (Informal)
[Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment](https://aclanthology.org/W19-5049) (Bhaskar et al., BioNLP 2019)
ACL