Papers by Young-Bum Kim
Learning Slice-Aware Representations with Mixture of Attentions (2021.findings-acl)
Copied to clipboard
| Challenge: | Real-world machine learning systems are achieving excellent performance in terms of coarse-grained metrics like overall accuracy and F-1 score. |
| Approach: | They extend slice-based learning (SBL) with a mixture of attentions to learn slice-aware dual attentive representations. |
| Outcome: | The proposed approach outperforms the baseline method and the original SBL approach on monitored slices with two natural language understanding tasks. |
A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems (2021.emnlp-main)
Copied to clipboard
Sunghyun Park, Han Li, Ameen Patel, Sidharth Mudgal, Sungjin Lee, Young-Bum Kim, Spyros Matsoukas, Ruhi Sarikaya
| Challenge: | Existing methods to improve NLU are laborintensive and expensive. |
| Approach: | They propose a scalable and automatic approach to improving NLU in a large-scale conversational AI system by leveraging implicit user feedback. |
| Outcome: | The proposed framework improves NLU in a large-scale conversational AI system across 10 domains. |
A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding (N18-3)
Copied to clipboard
| 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. |
AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation (2021.acl-long)
Copied to clipboard
| Challenge: | Large-scale conversational systems typically generate unnatural, robotic responses using template-based approaches. |
| Approach: | They propose a data augmentation approach that combines a self-trained neural retrieval model with a few-shot learned NLU model to automatically create MR-to-Text data from open-domain texts. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on the FewshotWOZ data in both BLEU and Slot Error Rate. |
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents (2021.naacl-main)
Copied to clipboard
| Challenge: | End-to-end deep learning methods that focus on user satisfaction are challenging due to the required annotation costs and turnaround times. |
| Approach: | They propose a self-supervised contrastive learning approach that leverages the pool of unlabeled data to learn user-agent interactions. |
| Outcome: | The proposed approach reduces the required number of annotations while improving generalization on unseen skills. |
Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding (N19-2)
Copied to clipboard
| Challenge: | Existing approaches to leveraging data across locales to improve domain classification accuracy are ineffective. |
| Approach: | They propose a locale-agnostic universal domain classification model that leverages available data across locales sharing the same language to improve domain classification accuracy. |
| Outcome: | The proposed model outperforms baseline models especially when classifying locale-specific domains and low-resourced domains. |
Supervised Domain Enablement Attention for Personalized Domain Classification (D18-1)
Copied to clipboard
| Challenge: | Recent IPDAs cover more than several thousands of diverse domains including Alexa Skills, Google Actions, and Cortana Skills. |
| Approach: | They propose a supervised enablement attention mechanism that utilizes sigmoid activation for the attention weighting and self-distillation to leverage the attention information of other enabled domains. |
| Outcome: | The proposed approach improves domain classification performance on real-world domains. |
Character-Level Feature Extraction with Densely Connected Networks (C18-1)
Copied to clipboard
| Challenge: | Existing methods to generate character-level features with neural architectures such as CNN or Recurrent Neural Network (RNN) are slow and generate position-independent features. |
| Approach: | They propose a method that uses a densely connected network to extract character-level features from words using CNN and RNN. |
| Outcome: | The proposed method shows robustness and effectiveness while being faster than CNN- or RNN-based methods. |
Efficient Large-Scale Neural Domain Classification with Personalized Attention (P18-1)
Copied to clipboard
| 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. |
Continuous Learning for Large-scale Personalized Domain Classification (N19-1)
Copied to clipboard
| Challenge: | Domain classification is the task to map spoken language utterances to one of the natural language understanding domains in intelligent personal digital assistants. |
| Approach: | They propose a neural-based approach for continuous domain adaption with normalization and regularization to accommodate new domains. |
| Outcome: | The proposed approach outperforms baseline methods on accommodated new domains and existing known domains by a large margin. |