Efficient Large-Scale Neural Domain Classification with Personalized Attention (P18-1)
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| Challenge: | Using a scalable neural model, we show that personalization improves domain classification accuracy in a setting with thousands of overlapping domains. |
| Approach: | They propose a scalable neural model architecture with a shared encoder that incorporates personalization information and domain-specific classifiers that solves the problem efficiently. |
| Outcome: | The proposed architecture achieves two orders of magnitude faster than full model retraining. |
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| Challenge: | Domain classification is the task to map spoken language utterances to one of the natural language understanding domains in intelligent personal digital assistants. |
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| Challenge: | Existing language modeling tools for automatic speech recognition (ASR) are difficult to personalize. |
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Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
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| Challenge: | Large language models (LLMs) have shown strong effectiveness and robustness when fine-tuned as dense retrievers. |
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| Challenge: | Neural Language Models (LMs) trained on large generic training sets have been shown to be effective at adapting to smaller, specific target domains for language modeling and other downstream tasks. |
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