Papers by Samhita Honnavalli
Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation (2022.lrec-1)
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Samhita Honnavalli, Aesha Parekh, Lily Ou, Sophie Groenwold, Sharon Levy, Vicente Ordonez, William Yang Wang
| Challenge: | Existing studies have not investigated how gender biases in natural language processing (NLP) are compounded with other societal biase. |
| Approach: | They propose a framework for probing compound bias by examining seniority in pre-trained neural generation models. |
| Outcome: | The proposed framework amplifies bias by considering women as junior and men as senior more often than ground truth in both domains. |
Investigating African-American Vernacular English in Transformer-Based Text Generation (2020.emnlp-main)
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Sophie Groenwold, Lily Ou, Aesha Parekh, Samhita Honnavalli, Sharon Levy, Diba Mirza, William Yang Wang
| Challenge: | Recent work in Natural Language Generation (NLG) uses a Transformer-based language model to generate high-quality, coherent text when prompted by arbitrary input. |
| Approach: | They evaluate the performance of a Transformer-based model that generates high-quality, coherent text when prompted by arbitrary input. |
| Outcome: | The proposed model improves on AAVE and SAE text with pretrained sentiment classifiers. |