Papers by Rajat Kumar
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering (2022.naacl-main)
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| Challenge: | Existing approaches to intent detection rely on epoch wise clustering and classification based on labeled and unlabeled data. |
| Approach: | They propose an end-to-end deep contrastive clustering algorithm that jointly updates model parameters and cluster centers via supervised and self-supervised learning. |
| Outcome: | The proposed approach outperforms baselines on five public datasets and human-in-the-loop variant for practical deployment. |
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection (2022.findings-naacl)
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| Challenge: | Existing methods to identify all possible user intents at design time are expensive and require storage of past data. |
| Approach: | They propose to continually train an intent detector on new intents while maintaining performance on prior intents. |
| Outcome: | The proposed method outperforms exemplar replay-based approaches on lifelong intent detection tasks and achieves state-of-the-art on four public datasets. |
Iterative Stratified Testing and Measurement for Automated Model Updates (2022.emnlp-industry)
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Elizabeth Dekeyser, Nicholas Comment, Shermin Pei, Rajat Kumar, Shruti Rai, Fengtao Wu, Lisa Haverty, Kanna Shimizu
| Challenge: | Automating updates to machine learning systems is an important but understudied challenge in AutoML. |
| Approach: | They propose a framework that relies on iterative model building coupled with data-shape stratified model testing and improvement to improve model accuracy. |
| Outcome: | The proposed framework shows a 26% improvement in accuracy for new model use cases on a large-scale NLU system compared to a naive baseline and current cutting-edge methods. |