@inproceedings{jin-etal-2022-causalnlp,
title = "{C}ausal{NLP} Tutorial: An Introduction to Causality for Natural Language Processing",
author = "Jin, Zhijing and
Feder, Amir and
Zhang, Kun",
editor = "El-Beltagy, Samhaa R. and
Qiu, Xipeng",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
month = dec,
year = "2022",
address = "Abu Dubai, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-tutorials.4",
doi = "10.18653/v1/2022.emnlp-tutorials.4",
pages = "17--22",
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.",
}
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%0 Conference Proceedings
%T CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing
%A Jin, Zhijing
%A Feder, Amir
%A Zhang, Kun
%Y El-Beltagy, Samhaa R.
%Y Qiu, Xipeng
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dubai, UAE
%F jin-etal-2022-causalnlp
%X 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.
%R 10.18653/v1/2022.emnlp-tutorials.4
%U https://aclanthology.org/2022.emnlp-tutorials.4
%U https://doi.org/10.18653/v1/2022.emnlp-tutorials.4
%P 17-22
Markdown (Informal)
[CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing](https://aclanthology.org/2022.emnlp-tutorials.4) (Jin et al., EMNLP 2022)
ACL