CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing

Zhijing Jin, Amir Feder, Kun Zhang


Abstract
Causal inference is becoming an increasingly important topic in deep learning, with the potential to help with critical deep learning problems such as model robustness, interpretability, and fairness. In addition, causality is naturally widely used in various disciplines of science, to discover causal relationships among variables and estimate causal effects of interest. In this tutorial, we introduce the fundamentals of causal discovery and causal effect estimation to the natural language processing (NLP) audience, provide an overview of causal perspectives to NLP problems, and aim to inspire novel approaches to NLP further. This tutorial is inclusive to a variety of audiences and is expected to facilitate the community’s developments in formulating and addressing new, important NLP problems in light of emerging causal principles and methodologies.
Anthology ID:
2022.emnlp-tutorials.4
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
Month:
December
Year:
2022
Address:
Abu Dubai, UAE
Editors:
Samhaa R. El-Beltagy, Xipeng Qiu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
17–22
Language:
URL:
https://aclanthology.org/2022.emnlp-tutorials.4
DOI:
10.18653/v1/2022.emnlp-tutorials.4
Bibkey:
Cite (ACL):
Zhijing Jin, Amir Feder, and Kun Zhang. 2022. CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, pages 17–22, Abu Dubai, UAE. Association for Computational Linguistics.
Cite (Informal):
CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing (Jin et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-tutorials.4.pdf