@inproceedings{takahashi-etal-2019-cler,
title = "{CLER}: Cross-task Learning with Expert Representation to Generalize Reading and Understanding",
author = "Takahashi, Takumi and
Taniguchi, Motoki and
Taniguchi, Tomoki and
Ohkuma, Tomoko",
editor = "Fisch, Adam and
Talmor, Alon and
Jia, Robin and
Seo, Minjoon and
Choi, Eunsol and
Chen, Danqi",
booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5824",
doi = "10.18653/v1/D19-5824",
pages = "183--190",
abstract = "This paper describes our model for the reading comprehension task of the MRQA shared task. We propose CLER, which stands for Cross-task Learning with Expert Representation for the generalization of reading and understanding. To generalize its capabilities, the proposed model is composed of three key ideas: multi-task learning, mixture of experts, and ensemble. In-domain datasets are used to train and validate our model, and other out-of-domain datasets are used to validate the generalization of our model{'}s performances. In a submission run result, the proposed model achieved an average F1 score of 66.1 {\%} in the out-of-domain setting, which is a 4.3 percentage point improvement over the official BERT baseline model.",
}
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<abstract>This paper describes our model for the reading comprehension task of the MRQA shared task. We propose CLER, which stands for Cross-task Learning with Expert Representation for the generalization of reading and understanding. To generalize its capabilities, the proposed model is composed of three key ideas: multi-task learning, mixture of experts, and ensemble. In-domain datasets are used to train and validate our model, and other out-of-domain datasets are used to validate the generalization of our model’s performances. In a submission run result, the proposed model achieved an average F1 score of 66.1 % in the out-of-domain setting, which is a 4.3 percentage point improvement over the official BERT baseline model.</abstract>
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%0 Conference Proceedings
%T CLER: Cross-task Learning with Expert Representation to Generalize Reading and Understanding
%A Takahashi, Takumi
%A Taniguchi, Motoki
%A Taniguchi, Tomoki
%A Ohkuma, Tomoko
%Y Fisch, Adam
%Y Talmor, Alon
%Y Jia, Robin
%Y Seo, Minjoon
%Y Choi, Eunsol
%Y Chen, Danqi
%S Proceedings of the 2nd Workshop on Machine Reading for Question Answering
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F takahashi-etal-2019-cler
%X This paper describes our model for the reading comprehension task of the MRQA shared task. We propose CLER, which stands for Cross-task Learning with Expert Representation for the generalization of reading and understanding. To generalize its capabilities, the proposed model is composed of three key ideas: multi-task learning, mixture of experts, and ensemble. In-domain datasets are used to train and validate our model, and other out-of-domain datasets are used to validate the generalization of our model’s performances. In a submission run result, the proposed model achieved an average F1 score of 66.1 % in the out-of-domain setting, which is a 4.3 percentage point improvement over the official BERT baseline model.
%R 10.18653/v1/D19-5824
%U https://aclanthology.org/D19-5824
%U https://doi.org/10.18653/v1/D19-5824
%P 183-190
Markdown (Informal)
[CLER: Cross-task Learning with Expert Representation to Generalize Reading and Understanding](https://aclanthology.org/D19-5824) (Takahashi et al., 2019)
ACL