Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models
This addresses the need for comparative evaluation in HLTM systems, which is incremental as it builds on existing frameworks.
The paper tackled the problem of evaluating Human-in-the-Loop Topic Modeling systems by comparing three approaches, finding that informed prior-based methods offer better user control while constraints produce higher quality topics.
To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulation experiments. These approaches extend previously proposed frameworks, including constraints and informed prior-based methods. Users should have a sense of control in HLTM systems, so we propose a control metric to measure whether refinement operations' results match users' expectations. Informed prior-based methods provide better control than constraints, but constraints yield higher quality topics.