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The promise of interaction between intelligent conversational agents andhumans is that models can learn from such feedback in order to improve.Unfortunately, such exchanges in the wild will not always involve humanutterances that are benign or of high quality, and will include a mixture ofengaged (helpers) and unengaged or even malicious users (trolls). In this workwe study how to perform robust learning in such an environment. We introduce abenchmark evaluation, SafetyMix, which can evaluate methods that learn safe vs.toxic language in a variety of adversarial settings to test their robustness.We propose and analyze several mitigating learning algorithms that identifytrolls either at the example or at the user level. Our main finding is thatuser-based methods, that take into account that troll users will exhibitadversarial behavior across multiple examples, work best in a variety ofsettings on our benchmark. We then test these methods in a further real-lifesetting of conversations collected during deployment, with similar results.

Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring the trolls