98 citations · 107 across the 4 of their papers we have counts for
6 papers
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies
Sunipa Dev, Masoud Monajatipoor, Anaelia Ovalle +3
Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models. However, current discussions primarily treat gender as…
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
Archit Rathore, Sunipa Dev, Jeff M. Phillips +6
Word vector embeddings have been shown to contain and amplify biases in data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and at…
The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability
Sunipa Dev
High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining. T…
On Measuring and Mitigating Biased Inferences of Word Embeddings
Sunipa Dev, Tao Li, Jeff Phillips +1
Word embeddings carry stereotypical connotations from the text they are trained on, which can lead to invalid inferences in downstream models that rely on them. We use this observa…
Attenuating Bias in Word Vectors
Sunipa Dev, Jeff Phillips
Word vector representations are well developed tools for various NLP and Machine Learning tasks and are known to retain significant semantic and syntactic structure of languages. B…
Closed Form Word Embedding Alignment
Sunipa Dev, Safia Hassan, Jeff M. Phillips
We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our met…