35 citations · 149 across the 13 of their papers we have counts for
4 papers · 1 filter
Nudging Neural Conversational Model with Domain Knowledge
Sungjin Lee
Neural conversation models are attractive because one can train a model directly on dialog examples with minimal labeling. With a small amount of data, however, they often fail to…
Zero-Shot Adaptive Transfer for Conversational Language Understanding
Sungjin Lee, Rahul Jha
Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains. A massive amount of labeled da…
OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding
Young-Bum Kim, Sungjin Lee, Karl Stratos
In practice, most spoken language understanding systems process user input in a pipelined manner; first domain is predicted, then intent and semantic slots are inferred according t…
Toward Continual Learning for Conversational Agents
Sungjin Lee
While end-to-end neural conversation models have led to promising advances in reducing hand-crafted features and errors induced by the traditional complex system architecture, they…