Papers by Brandon Lwowski

2 papers
Measuring Geographic Performance Disparities of Offensive Language Classifiers (2022.coling-1)

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Challenge: Recent work shows that text classifiers are biased regarding different languages and dialects.
Approach: They propose to use a dataset to examine whether language, dialect, and topical content vary across geographical regions to address these gaps.
Outcome: The proposed dataset includes 14 thousand examples across 15 cities and shows that current models do not generalize across locations.
An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings (2020.coling-main)

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Challenge: Pre-trained word embeddings have been shown to improve the performance of neural networks across a wide variety of tasks.
Approach: They propose two new metrics to understand the downstream reliability of word embeddings.
Outcome: The proposed model can improve performance with slight changes to the training data, but it can also fail with multiple neural network architectures.

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