Papers by Robert Stanforth
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation (D19-1)
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Po-Sen Huang, Robert Stanforth, Johannes Welbl, Chris Dyer, Dani Yogatama, Sven Gowal, Krishnamurthy Dvijotham, Pushmeet Kohli
| Challenge: | Recent work has exposed the vulnerabilities of neural NLP models, e.g. with small, semantically invariant input alterations. |
| Approach: | They propose to model text classification under synonym replacements or character flip perturbations and then use a formal model verification method to verify its robustness. |
| Outcome: | The proposed models show little difference in terms of nominal accuracy, but have much improved verified accuracy under perturbations and come with an efficiently computable formal guarantee on worst case adversaries. |
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation (2020.findings-emnlp)
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Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, Pushmeet Kohli
| Challenge: | Language modeling has advanced rapidly due to efficient model architectures and the availability of large text corpora. |
| Approach: | They propose to embed and regularize sentiment prediction-derived regularizations on the language model’s latent representations to reduce bias in the sentiment of generated text. |
| Outcome: | The proposed methods reduce bias in the sentiment of generated text by adopting individual and group fairness metrics from the fair machine learning literature. |