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        <title>RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms</title>
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    <abstract>Pre-trained language models (PTLMs) have achieved impressive performance on commonsense inference benchmarks, but their ability to employ commonsense to make robust inferences, which is crucial for effective communications with humans, is debated. In the pursuit of advancing fluid human-AI communication, we propose a new challenge, RICA: Robust Inference using Commonsense Axioms, that evaluates robust commonsense inference despite textual perturbations. To generate data for this challenge, we develop a systematic and scalable procedure using commonsense knowledge bases and probe PTLMs across two different evaluation settings. Extensive experiments on our generated probe sets with more than 10k statements show that PTLMs perform no better than random guessing on the zero-shot setting, are heavily impacted by statistical biases, and are not robust to perturbation attacks. We also find that fine-tuning on similar statements offer limited gains, as PTLMs still fail to generalize to unseen inferences. Our new large-scale benchmark exposes a significant gap between PTLMs and human-level language understanding and offers a new challenge for PTLMs to demonstrate commonsense.</abstract>
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%0 Conference Proceedings
%T RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms
%A Zhou, Pei
%A Khanna, Rahul
%A Lee, Seyeon
%A Lin, Bill Yuchen
%A Ho, Daniel
%A Pujara, Jay
%A Ren, Xiang
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F zhou-etal-2021-rica
%X Pre-trained language models (PTLMs) have achieved impressive performance on commonsense inference benchmarks, but their ability to employ commonsense to make robust inferences, which is crucial for effective communications with humans, is debated. In the pursuit of advancing fluid human-AI communication, we propose a new challenge, RICA: Robust Inference using Commonsense Axioms, that evaluates robust commonsense inference despite textual perturbations. To generate data for this challenge, we develop a systematic and scalable procedure using commonsense knowledge bases and probe PTLMs across two different evaluation settings. Extensive experiments on our generated probe sets with more than 10k statements show that PTLMs perform no better than random guessing on the zero-shot setting, are heavily impacted by statistical biases, and are not robust to perturbation attacks. We also find that fine-tuning on similar statements offer limited gains, as PTLMs still fail to generalize to unseen inferences. Our new large-scale benchmark exposes a significant gap between PTLMs and human-level language understanding and offers a new challenge for PTLMs to demonstrate commonsense.
%R 10.18653/v1/2021.emnlp-main.598
%U https://aclanthology.org/2021.emnlp-main.598/
%U https://doi.org/10.18653/v1/2021.emnlp-main.598
%P 7560-7579

Markdown (Informal)

[RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms](https://aclanthology.org/2021.emnlp-main.598/) (Zhou et al., EMNLP 2021)

• RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms (Zhou et al., EMNLP 2021)

ACL

• Pei Zhou, Rahul Khanna, Seyeon Lee, Bill Yuchen Lin, Daniel Ho, Jay Pujara, and Xiang Ren. 2021. RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7560–7579, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.