In the review paper of causal reasoning in natural language processing, 13 NLP giants such as Israel Institute of technology and Google expounded the estimation, prediction, interpretation and Transcendence of causal reasoning NLP

Zhiyuan community 2021-09-15 08:44:52

With the in-depth use of in-depth learning in the industry , The previous algorithm model based on correlation shows defects , Such as robustness 、 Interpretability 、 Fairness, etc . Recently from Israel Institute of Technology 、 Stanford 、Google Wait for the release of 《 Causal reasoning in natural language processing 》 The paper of , Describe the estimation of causal reasoning in natural language processing 、 forecast 、 Interpretable, etc .

Abstract :

A basic goal of scientific research is to understand causality . However , Although causality plays a key role in life and Social Sciences , But in natural language processing (NLP) There is no equal importance in , The latter traditionally pays more attention to prediction tasks . With the rise of interdisciplinary research on the integration of causal reasoning and language processing , This difference is beginning to disappear . However , About NLP The study of causality in is still scattered in various fields , There is no uniform definition 、 Benchmark data sets and clear statements of remaining challenges .

In this review , We have consolidated cross academic research , And put it in a broader NLP In the landscape . We introduce the statistical challenge of estimating causal effects , Include text as a result 、 The setting of means to treat or solve confusion . Besides , We also explore the potential use of causal reasoning , To improve NLP The performance of the model 、 Robustness 、 Fairness and interpretability . therefore , We provide a unified overview of causal reasoning for the field of computational linguistics .

Address of thesis :https://arxiv.org/abs/2109.00725

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