TopGuNN: Fast NLP Training Data Augmentation using Large Corpora

Rebecca Iglesias-Flores, Megha Mishra, Ajay Patel, Akanksha Malhotra, Reno Kriz, Martha Palmer, Chris Callison-Burch


Abstract
Acquiring training data for natural language processing systems can be expensive and time-consuming. Given a few training examples crafted by experts, large corpora can be mined for thousands of semantically similar examples that provide useful variability to improve model generalization. We present TopGuNN, a fast contextualized k-NN retrieval system that can efficiently index and search over contextual embeddings generated from large corpora. TopGuNN is demonstrated for a training data augmentation use case over the Gigaword corpus. Using approximate k-NN and an efficient architecture, TopGuNN performs queries over an embedding space of 4.63TB (approximately 1.5B embeddings) in less than a day.
Anthology ID:
2021.dash-1.14
Volume:
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances
Month:
June
Year:
2021
Address:
Online
Venues:
DaSH | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
86–101
Language:
URL:
https://aclanthology.org/2021.dash-1.14
DOI:
10.18653/v1/2021.dash-1.14
Bibkey:
Cite (ACL):
Rebecca Iglesias-Flores, Megha Mishra, Ajay Patel, Akanksha Malhotra, Reno Kriz, Martha Palmer, and Chris Callison-Burch. 2021. TopGuNN: Fast NLP Training Data Augmentation using Large Corpora. In Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances, pages 86–101, Online. Association for Computational Linguistics.
Cite (Informal):
TopGuNN: Fast NLP Training Data Augmentation using Large Corpora (Iglesias-Flores et al., DaSH 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.dash-1.14.pdf
Code
 penn-topgunn/topgunn