Syntax in End-to-End Natural Language Processing

Hai Zhao, Rui Wang, Kehai Chen


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.
Anthology ID:
2021.emnlp-tutorials.6
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic & Online
Editors:
Jing Jiang, Ivan Vulić
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
27–33
Language:
URL:
https://aclanthology.org/2021.emnlp-tutorials.6
DOI:
10.18653/v1/2021.emnlp-tutorials.6
Bibkey:
Cite (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.
Cite (Informal):
Syntax in End-to-End Natural Language Processing (Zhao et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-tutorials.6.pdf