Papers with domain classification
Noise Robust Named Entity Understanding for Voice Assistants (2021.naacl-industry)
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Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D Williams, Lin Li
| Challenge: | Named Entity Recognition and Entity Linking are challenging for voice assistants . utterances are relatively short, so there is not much context to help disambiguate . |
| Approach: | They propose a Named Entity Understanding system that combines NER and EL in a joint reranking module. |
| Outcome: | The proposed framework improves NER accuracy by up to 3.13% and EL accuracy by 3.6% in F1 score . it also leads to better accuracies in other natural language understanding tasks . |
Supervised Domain Enablement Attention for Personalized Domain Classification (D18-1)
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| 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. |
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. |
Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language (C18-1)
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| Challenge: | a study aims to develop a language transferring system to avoid the trouble of acquiring and labeling a new big SLU corpus . general-purpose translators cannot handle the lot of semantic labels, not to mention cultural differences . a RL-based language transfer method can be used to adapt the adapted translator to a target language . |
| Approach: | They propose to use reinforcement learning to adapt a spoken language understanding model to a target language. |
| Outcome: | The proposed language transferring method improves domain classification accuracy by 22% compared with naive translation . the proposed language transfer method can be used on Chinese to English translators with more proper slot tags . |
Continuous Learning for Large-scale Personalized Domain Classification (N19-1)
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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. |
| 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. |
A Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding (2025.naacl-long)
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| Challenge: | Existing approaches to DST are limited by their computational resources or lack flexibility to adapt to new slots. |
| Approach: | They propose a system that integrates domain classification and DST in a single pipeline and uses self-refining prompts to adapt dynamically. |
| Outcome: | The proposed system improves on existing methods on multiWOZ datasets and provides 20% better Joint Goal Accuracy (JGA) over existing methods with 90% fewer requests to the LLM API. |