@inproceedings{zhao-etal-2021-syntax,
title = "Syntax in End-to-End Natural Language Processing",
author = "Zhao, Hai and
Wang, Rui and
Chen, Kehai",
editor = "Jiang, Jing and
Vuli{\'c}, Ivan",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic {\&} Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-tutorials.6",
doi = "10.18653/v1/2021.emnlp-tutorials.6",
pages = "27--33",
abstract = "This tutorial surveys the latest technical progress of syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks, in which semantic role labeling (SRL) and machine translation (MT) are the representative NLP tasks that have always been beneficial from informative syntactic clues since a long time ago, though the advance from end-to-end deep learning models shows new results. In this tutorial, we will first introduce the background and the latest progress of syntactic parsing and SRL/NMT. Then, we will summarize the key evidence about the syntactic impacts over these two concerning tasks, and explore the behind reasons from both computational and linguistic backgrounds.",
}
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%0 Conference Proceedings
%T Syntax in End-to-End Natural Language Processing
%A Zhao, Hai
%A Wang, Rui
%A Chen, Kehai
%Y Jiang, Jing
%Y Vulić, Ivan
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic & Online
%F zhao-etal-2021-syntax
%X This tutorial surveys the latest technical progress of syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks, in which semantic role labeling (SRL) and machine translation (MT) are the representative NLP tasks that have always been beneficial from informative syntactic clues since a long time ago, though the advance from end-to-end deep learning models shows new results. In this tutorial, we will first introduce the background and the latest progress of syntactic parsing and SRL/NMT. Then, we will summarize the key evidence about the syntactic impacts over these two concerning tasks, and explore the behind reasons from both computational and linguistic backgrounds.
%R 10.18653/v1/2021.emnlp-tutorials.6
%U https://aclanthology.org/2021.emnlp-tutorials.6
%U https://doi.org/10.18653/v1/2021.emnlp-tutorials.6
%P 27-33
Markdown (Informal)
[Syntax in End-to-End Natural Language Processing](https://aclanthology.org/2021.emnlp-tutorials.6) (Zhao et al., EMNLP 2021)
ACL
- Hai Zhao, Rui Wang, and Kehai Chen. 2021. Syntax in End-to-End Natural Language Processing. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, pages 27–33, Punta Cana, Dominican Republic & Online. Association for Computational Linguistics.