Deep Active Learning for Text Classification with Diverse Interpretations-Reference-Cited by-同舟云学术

Deep Active Learning for Text Classification with Diverse Interpretations

Published:2021-10-26 Issue: Volume: Page:
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Container-title:Proceedings of the 30th ACM International Conference on Information & Knowledge Management
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Author:

Liu Qiang¹,Zhu Yanqiao¹,Liu Zhaocheng²,Zhang Yufeng³,Wu Shu⁴

Affiliation:

1. Institute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China

2. RealAI, Beijing, China

3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

4. Institute of Automation, Chinese Academy of SciencesInstitute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3459637.3482080

Reference32 articles.

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2. Lingyang Chu Xia Hu Juhua Hu Lanjun Wang and Jian Pei. 2018. Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution. In KDD. 1244--1253. Lingyang Chu Xia Hu Juhua Hu Lanjun Wang and Jian Pei. 2018. Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution. In KDD. 1244--1253.

3. Liat Ein-Dor Alon Halfon Ariel Gera Eyal Shnarch Lena Dankin Leshem Choshen Marina Danilevsky Ranit Aharonov Yoav Katz and Noam Slonim. 2020. Active Learning for BERT: An Empirical Study. In EMNLP. 7949--7962. Liat Ein-Dor Alon Halfon Ariel Gera Eyal Shnarch Lena Dankin Leshem Choshen Marina Danilevsky Ranit Aharonov Yoav Katz and Noam Slonim. 2020. Active Learning for BERT: An Empirical Study. In EMNLP. 7949--7962.

4. Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In ICML. 1050--1059. Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In ICML. 1050--1059.

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