@inproceedings{rooshenas-etal-2018-training,
title = "Training Structured Prediction Energy Networks with Indirect Supervision",
author = "Rooshenas, Amirmohammad and
Kamath, Aishwarya and
McCallum, Andrew",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2021",
doi = "10.18653/v1/N18-2021",
pages = "130--135",
abstract = "This paper introduces rank-based training of structured prediction energy networks (SPENs). Our method samples from output structures using gradient descent and minimizes the ranking violation of the sampled structures with respect to a scalar scoring function defined with domain knowledge. We have successfully trained SPEN for citation field extraction without any labeled data instances, where the only source of supervision is a simple human-written scoring function. Such scoring functions are often easy to provide; the SPEN then furnishes an efficient structured prediction inference procedure.",
}
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%0 Conference Proceedings
%T Training Structured Prediction Energy Networks with Indirect Supervision
%A Rooshenas, Amirmohammad
%A Kamath, Aishwarya
%A McCallum, Andrew
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F rooshenas-etal-2018-training
%X This paper introduces rank-based training of structured prediction energy networks (SPENs). Our method samples from output structures using gradient descent and minimizes the ranking violation of the sampled structures with respect to a scalar scoring function defined with domain knowledge. We have successfully trained SPEN for citation field extraction without any labeled data instances, where the only source of supervision is a simple human-written scoring function. Such scoring functions are often easy to provide; the SPEN then furnishes an efficient structured prediction inference procedure.
%R 10.18653/v1/N18-2021
%U https://aclanthology.org/N18-2021
%U https://doi.org/10.18653/v1/N18-2021
%P 130-135
Markdown (Informal)
[Training Structured Prediction Energy Networks with Indirect Supervision](https://aclanthology.org/N18-2021) (Rooshenas et al., NAACL 2018)
ACL
- Amirmohammad Rooshenas, Aishwarya Kamath, and Andrew McCallum. 2018. Training Structured Prediction Energy Networks with Indirect Supervision. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 130–135, New Orleans, Louisiana. Association for Computational Linguistics.