Adrian Cheung


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Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods
Stefan Larson | Adrian Cheung | Anish Mahendran | Kevin Leach | Jonathan K. Kummerfeld
Proceedings of the 28th International Conference on Computational Linguistics

Slot-filling models in task-driven dialog systems rely on carefully annotated training data. However, annotations by crowd workers are often inconsistent or contain errors. Simple solutions like manually checking annotations or having multiple workers label each sample are expensive and waste effort on samples that are correct. If we can identify inconsistencies, we can focus effort where it is needed. Toward this end, we define six inconsistency types in slot-filling annotations. Using three new noisy crowd-annotated datasets, we show that a wide range of inconsistencies occur and can impact system performance if not addressed. We then introduce automatic methods of identifying inconsistencies. Experiments on our new datasets show that these methods effectively reveal inconsistencies in data, though there is further scope for improvement.

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Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness
Stefan Larson | Anthony Zheng | Anish Mahendran | Rishi Tekriwal | Adrian Cheung | Eric Guldan | Kevin Leach | Jonathan K. Kummerfeld
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Diverse data is crucial for training robust models, but crowdsourced text often lacks diversity as workers tend to write simple variations from prompts. We propose a general approach for guiding workers to write more diverse text by iteratively constraining their writing. We show how prior workflows are special cases of our approach, and present a way to apply the approach to dialog tasks such as intent classification and slot-filling. Using our method, we create more challenging versions of test sets from prior dialog datasets and find dramatic performance drops for standard models. Finally, we show that our approach is complementary to recent work on improving data diversity, and training on data collected with our approach leads to more robust models.