Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation

Rishi Hazra, Parag Dutta, Shubham Gupta, Mohammed Abdul Qaathir, Ambedkar Dukkipati


Abstract
While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and time-consuming. Active Learning (AL) strategies reduce the need for huge volumes of labeled data by iteratively selecting a small number of examples for manual annotation based on their estimated utility in training the given model. In this paper, we argue that since AL strategies choose examples independently, they may potentially select similar examples, all of which may not contribute significantly to the learning process. Our proposed approach, Active2 Learning (A2L), actively adapts to the deep learning model being trained to eliminate such redundant examples chosen by an AL strategy. We show that A2L is widely applicable by using it in conjunction with several different AL strategies and NLP tasks. We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by 3-25% on an absolute scale on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead.
Anthology ID:
2021.naacl-main.159
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1982–1995
Language:
URL:
https://aclanthology.org/2021.naacl-main.159
DOI:
10.18653/v1/2021.naacl-main.159
Bibkey:
Cite (ACL):
Rishi Hazra, Parag Dutta, Shubham Gupta, Mohammed Abdul Qaathir, and Ambedkar Dukkipati. 2021. Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1982–1995, Online. Association for Computational Linguistics.
Cite (Informal):
Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation (Hazra et al., NAACL 2021)
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PDF:
https://aclanthology.org/2021.naacl-main.159.pdf
Video:
 https://aclanthology.org/2021.naacl-main.159.mp4