Search Results for author:
Found 4 papers, 1 papers with code
Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness
Diverse data is crucial for training robust models, but crowdsourced text often lacks diversity as workers tend to write simple variations from prompts.
Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods
Using three new noisy crowd-annotated datasets, we show that a wide range of inconsistencies occur and can impact system performance if not addressed.
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries.
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems
We also present a novel data collection pipeline built atop our detection technique to automatically and iteratively mine unique data samples while discarding erroneous samples.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
