Tanya Roosta


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QCon at SemEval-2023 Task 10: Data Augmentation and Model Ensembling for Detection of Online Sexism
Weston Feely | Prabhakar Gupta | Manas Ranjan Mohanty | Timothy Chon | Tuhin Kundu | Vijit Singh | Sandeep Atluri | Tanya Roosta | Viviane Ghaderi | Peter Schulam
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

The web contains an abundance of user- generated content. While this content is useful for many applications, it poses many challenges due to the presence of offensive, biased, and overall toxic language. In this work, we present a system that identifies and classifies sexist content at different levels of granularity. Using transformer-based models, we explore the value of data augmentation, use of ensemble methods, and leverage in-context learning using foundation models to tackle the task. We evaluate the different components of our system both quantitatively and qualitatively. Our best systems achieve an F1 score of 0.84 for the binary classification task aiming to identify whether a given content is sexist or not and 0.64 and 0.47 for the two multi-class tasks that aim to identify the coarse and fine-grained types of sexism present in the given content respectively.


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Training Mixed-Domain Translation Models via Federated Learning
Peyman Passban | Tanya Roosta | Rahul Gupta | Ankit Chadha | Clement Chung
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Training mixed-domain translation models is a complex task that demands tailored architec- tures and costly data preparation techniques. In this work, we leverage federated learning (FL) in order to tackle the problem. Our investiga- tion demonstrates that with slight modifications in the training process, neural machine trans- lation (NMT) engines can be easily adapted when an FL-based aggregation is applied to fuse different domains. Experimental results also show that engines built via FL are able to perform on par with state-of-the-art baselines that rely on centralized training techniques. We evaluate our hypothesis in the presence of five datasets with different sizes, from different domains, to translate from German into English and discuss how FL and NMT can mutually benefit from each other. In addition to provid- ing benchmarking results on the union of FL and NMT, we also propose a novel technique to dynamically control the communication band- width by selecting impactful parameters during FL updates. This is a significant achievement considering the large size of NMT engines that need to be exchanged between FL parties.