Papers by Gokmen Oz
Towards Equitable Natural Language Understanding Systems for Dialectal Cohorts: Debiasing Training Data (2024.lrec-main)
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| Challenge: | Prior research has shown that biases exist in these models against certain languages or dialects. |
| Approach: | They propose to use a dialect identification model to obtain targeted training data augmentation for under-represented dialects to debias NLU model for dialectal cohorts in NLU systems. |
| Outcome: | The proposed framework can provide insights on dialect disparity in real-world NLU systems and targeted data argumentation can help narrow the model’s performance gap between standard language speakers and dialect speakers. |
Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks (2022.emnlp-industry)
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| Challenge: | Domain Classification (DC) and Intent Classification/Named Entity Recognition (ICNER) are the most common methods for reducing teacher-student knowledge into manageable sizes for low-latency downstream applications. |
| Approach: | They investigate whether distillation from a generic LM benefits downstream tasks . a domain classification and a task-specific data set are used to fine tune the model . |
| Outcome: | The proposed model improves across tasks and test sets when only task-specific data is used. |