Papers by Dongchan Kim
A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding (N18-3)
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| Challenge: | Existing approaches to classify a given utterance into domains are costly and time-consuming. |
| Approach: | They propose a shortlisting-reranking neural model for large-scale domain classification for IPDAs . they use extensive experiments on 1,500 IPDA domains to test their effectiveness . |
| Outcome: | The proposed model is tested on 1,500 IPDA domains. |
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. |