Ed Chi


pdf bib
Can We Improve Model Robustness through Secondary Attribute Counterfactuals?
Ananth Balashankar | Xuezhi Wang | Ben Packer | Nithum Thain | Ed Chi | Alex Beutel
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Developing robust NLP models that perform well on many, even small, slices of data is a significant but important challenge, with implications from fairness to general reliability. To this end, recent research has explored how models rely on spurious correlations, and how counterfactual data augmentation (CDA) can mitigate such issues. In this paper we study how and why modeling counterfactuals over multiple attributes can go significantly further in improving model performance. We propose RDI, a context-aware methodology which takes into account the impact of secondary attributes on the model’s predictions and increases sensitivity for secondary attributes over reweighted counterfactually augmented data. By implementing RDI in the context of toxicity detection, we find that accounting for secondary attributes can significantly improve robustness, with improvements in sliced accuracy on the original dataset up to 7% compared to existing robustness methods. We also demonstrate that RDI generalizes to the coreference resolution task and provide guidelines to extend this to other tasks.


pdf bib
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
Tianlu Wang | Xuezhi Wang | Yao Qin | Ben Packer | Kang Li | Jilin Chen | Alex Beutel | Ed Chi
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

NLP models are shown to suffer from robustness issues, i.e., a model’s prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that are known to be invariant to task labels. For example, in order to attack a model for sentiment classification over product reviews, we can use the product categories as the controllable attribute which would not change the sentiment of the reviews. Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches. We further use our generated adversarial examples to improve models through adversarial training, and we demonstrate that our generated attacks are more robust against model re-training and different model architectures.


pdf bib
Location and Language Use in Social Media
Ed Chi
Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science