Leah Nicolich-Henkin
2020
Extreme Model Compression for On-device Natural Language Understanding
Kanthashree Mysore Sathyendra
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Samridhi Choudhary
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Leah Nicolich-Henkin
Proceedings of the 28th International Conference on Computational Linguistics: Industry Track
In this paper, we propose and experiment with techniques for extreme compression of neural natural language understanding (NLU) models, making them suitable for execution on resource-constrained devices. We propose a task-aware, end-to-end compression approach that performs word-embedding compression jointly with NLU task learning. We show our results on a large-scale, commercial NLU system trained on a varied set of intents with huge vocabulary sizes. Our approach outperforms a range of baselines and achieves a compression rate of 97.4% with less than 3.7% degradation in predictive performance. Our analysis indicates that the signal from the downstream task is important for effective compression with minimal degradation in performance.
2016
A Wizard-of-Oz Study on A Non-Task-Oriented Dialog Systems That Reacts to User Engagement
Zhou Yu
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Leah Nicolich-Henkin
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Alan W Black
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Alexander Rudnicky
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Initiations and Interruptions in a Spoken Dialog System
Leah Nicolich-Henkin
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Carolyn Rosé
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Alan W Black
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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