@inproceedings{zhang-2018-joint,
title = "Joint models for {NLP}",
author = "Zhang, Yue",
editor = "{Mausam} and
Wang, Lu",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
month = oct # "-" # nov,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-3001",
abstract = "Joint models have received much research attention in NLP, allowing relevant tasks to share common information while avoiding error propagation in multi-stage pepelines. Several main approaches have been taken by statistical joint modeling, while neural models allow parameter sharing and adversarial training. This tutorial reviews main approaches to joint modeling for both statistical and neural methods.",
}
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<abstract>Joint models have received much research attention in NLP, allowing relevant tasks to share common information while avoiding error propagation in multi-stage pepelines. Several main approaches have been taken by statistical joint modeling, while neural models allow parameter sharing and adversarial training. This tutorial reviews main approaches to joint modeling for both statistical and neural methods.</abstract>
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%0 Conference Proceedings
%T Joint models for NLP
%A Zhang, Yue
%Y Wang, Lu
%E Mausam
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Melbourne, Australia
%F zhang-2018-joint
%X Joint models have received much research attention in NLP, allowing relevant tasks to share common information while avoiding error propagation in multi-stage pepelines. Several main approaches have been taken by statistical joint modeling, while neural models allow parameter sharing and adversarial training. This tutorial reviews main approaches to joint modeling for both statistical and neural methods.
%U https://aclanthology.org/D18-3001
Markdown (Informal)
[Joint models for NLP](https://aclanthology.org/D18-3001) (Zhang, EMNLP 2018)
ACL
- Yue Zhang. 2018. Joint models for NLP. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, Melbourne, Australia. Association for Computational Linguistics.