A Hybrid Intelligence Method for Argument Mining
This addresses the challenge of efficiently analyzing citizen feedback for policymakers or researchers, though it is incremental as it combines existing human and AI approaches.
The paper tackled the problem of extracting key arguments from large, noisy opinion corpora by proposing HyEnA, a hybrid human-AI method, which achieved higher coverage and precision than a state-of-the-art automated method while requiring less human effort than fully manual expert analysis.
Large-scale survey tools enable the collection of citizen feedback in opinion corpora. Extracting the key arguments from a large and noisy set of opinions helps in understanding the opinions quickly and accurately. Fully automated methods can extract arguments but (1) require large labeled datasets that induce large annotation costs and (2) work well for known viewpoints, but not for novel points of view. We propose HyEnA, a hybrid (human + AI) method for extracting arguments from opinionated texts, combining the speed of automated processing with the understanding and reasoning capabilities of humans. We evaluate HyEnA on three citizen feedback corpora. We find that, on the one hand, HyEnA achieves higher coverage and precision than a state-of-the-art automated method when compared to a common set of diverse opinions, justifying the need for human insight. On the other hand, HyEnA requires less human effort and does not compromise quality compared to (fully manual) expert analysis, demonstrating the benefit of combining human and artificial intelligence.