@inproceedings{fu-etal-2018-case,
title = "A Case Study on Learning a Unified Encoder of Relations",
author = "Fu, Lisheng and
Min, Bonan and
Nguyen, Thien Huu and
Grishman, Ralph",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6126",
doi = "10.18653/v1/W18-6126",
pages = "202--207",
abstract = "Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema. An individual corpus is often small, and the models may often be biased or overfitted to the corpus. We hypothesize that we can learn a better representation by combining multiple relation datasets. We attempt to use a shared encoder to learn the unified feature representation and to augment it with regularization by adversarial training. The additional corpora feeding the encoder can help to learn a better feature representation layer even though the relation schemas are different. We use ACE05 and ERE datasets as our case study for experiments. The multi-task model obtains significant improvement on both datasets.",
}
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<abstract>Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema. An individual corpus is often small, and the models may often be biased or overfitted to the corpus. We hypothesize that we can learn a better representation by combining multiple relation datasets. We attempt to use a shared encoder to learn the unified feature representation and to augment it with regularization by adversarial training. The additional corpora feeding the encoder can help to learn a better feature representation layer even though the relation schemas are different. We use ACE05 and ERE datasets as our case study for experiments. The multi-task model obtains significant improvement on both datasets.</abstract>
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%0 Conference Proceedings
%T A Case Study on Learning a Unified Encoder of Relations
%A Fu, Lisheng
%A Min, Bonan
%A Nguyen, Thien Huu
%A Grishman, Ralph
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F fu-etal-2018-case
%X Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema. An individual corpus is often small, and the models may often be biased or overfitted to the corpus. We hypothesize that we can learn a better representation by combining multiple relation datasets. We attempt to use a shared encoder to learn the unified feature representation and to augment it with regularization by adversarial training. The additional corpora feeding the encoder can help to learn a better feature representation layer even though the relation schemas are different. We use ACE05 and ERE datasets as our case study for experiments. The multi-task model obtains significant improvement on both datasets.
%R 10.18653/v1/W18-6126
%U https://aclanthology.org/W18-6126
%U https://doi.org/10.18653/v1/W18-6126
%P 202-207
Markdown (Informal)
[A Case Study on Learning a Unified Encoder of Relations](https://aclanthology.org/W18-6126) (Fu et al., WNUT 2018)
ACL
- Lisheng Fu, Bonan Min, Thien Huu Nguyen, and Ralph Grishman. 2018. A Case Study on Learning a Unified Encoder of Relations. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 202–207, Brussels, Belgium. Association for Computational Linguistics.