Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP-Reference-Cited by-同舟云学术

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Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP

Published:2021 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
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Author:

Jin Zhijing,von Kügelgen Julius,Ni Jingwei,Vaidhya Tejas,Kaushal Ayush,Sachan Mrinmaya,Schoelkopf Bernhard

Publisher

Association for Computational Linguistics

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Tackling Concept Shift in Text Classification using Entailment-style Modeling;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Implications of Minimum Description Length for Adversarial Attack in Natural Language Processing;Entropy;2024-04-24

3. Learning Entangled Interactions of Complex Causality via Self-Paced Contrastive Learning;ACM Transactions on Knowledge Discovery from Data;2023-12-09

4. Exploring the Effectiveness of Various Deep Learning Techniques for Text Generation in Natural Language Processing;2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT);2023-11-23

5. CauSSL: Causality-inspired Semi-supervised Learning for Medical Image Segmentation;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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