Papers with large-scale domain classification

2 papers
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.
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.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations