To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP | Citedby Results | Computational Linguistics | MIT Press

Skip to Main Content

Skip Nav Destination

To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP

Computational Linguistics (2022) 48 (1): 5–42.

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

DAT-MT Accelerated Graph Fusion Dependency Parsing Model for Small Samples in Professional Fields

Rui Li
Shili Shu
Shunli Wang
Yang Liu
Yanhao Li
Mingjun Peng

Entropy (2023) 25 (10): 1444.

Text augmentation for semantic frame induction and parsing

Saba Anwar
Artem Shelmanov
Nikolay Arefyev
Alexander Panchenko
Chris Biemann

Language Resources and Evaluation (2023)

A review on abusive content automatic detection: approaches, challenges and opportunities

Bedour Alrashidi
Amani Jamal
Imtiaz Khan
Ali Alkhathlan

PeerJ Computer Science (2022) 8: e1142.

Analytical Review of Methods for Solving Data Scarcity Issues Regarding Elaboration of Automatic Speech Recognition Systems for Low-Resource Languages

Irina Kipyatkova
Ildar Kagirov

Informatics and Automation (2022) 21 (4): 678.