Cicero: Multi-Turn, Contextual Argumentation for Accurate Crowdsourcing
This addresses the challenge of achieving high-accuracy crowdwork for difficult problems, particularly where traditional methods fail, by enabling targeted discussions, though it is incremental over existing argumentation systems.
The paper tackles the problem of low accuracy in crowdsourcing on difficult tasks by introducing Cicero, a workflow that uses multi-turn, contextual argumentation, resulting in improvements such as a 16.7 percentage point increase in worker accuracy on relation extraction and raising accuracy from 66.7% to 98.8% on the Codenames domain.
Traditional approaches for ensuring high quality crowdwork have failed to achieve high-accuracy on difficult problems. Aggregating redundant answers often fails on the hardest problems when the majority is confused. Argumentation has been shown to be effective in mitigating these drawbacks. However, existing argumentation systems only support limited interactions and show workers general justifications, not context-specific arguments targeted to their reasoning. This paper presents Cicero, a new workflow that improves crowd accuracy on difficult tasks by engaging workers in multi-turn, contextual discussions through real-time, synchronous argumentation. Our experiments show that compared to previous argumentation systems which only improve the average individual worker accuracy by 6.8 percentage points on the Relation Extraction domain, our workflow achieves 16.7 percentage point improvement. Furthermore, previous argumentation approaches don't apply to tasks with many possible answers; in contrast, Cicero works well in these cases, raising accuracy from 66.7% to 98.8% on the Codenames domain.