Summary of Baby Bear: Seeking a Just Right Rating Scale For Scalar Annotations, by Xu Han et al.
Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations
by Xu Han, Felix Yu, Joao Sedoc, Benjamin Van Durme
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel mechanism for efficiently assigning scalar ratings to each element in a large set is proposed. The goal is to enable effective evaluation of products or reviews based on user feedback. Prior work has shown that Best Worst Scaling (BWS) is more robust when sample sizes are small, but it is not suitable for large-scale tasks. To address this issue, the authors introduce IBWS, an iterative approach that collects annotations through BWS, resulting in robustly ranked crowd-sourced data. The study evaluates various direct assessment methods to determine what is both cost-efficient and best correlates with a large-scale BWS annotation strategy. The proposed approach can support robust learning-to-rank models in domains such as dialogue and sentiment analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to measure how people feel about things, like products or reviews, is developed. This method helps us understand what most people think by asking them which options are the best and worst. When we have a small number of opinions, this approach works well. But when we have many opinions, it becomes too expensive and time-consuming. To solve this problem, the researchers created a new method that uses the results from the first approach as a guide to find the most accurate way to measure opinions. This new method is shown to work well in areas like chatbots and understanding people’s emotions. |
