35 citations · 56 across the 11 of their papers we have counts for
3 papers · 1 filter
Improving End-to-End Speech-to-Intent Classification with Reptile
Yusheng Tian, Philip John Gorinski
End-to-end spoken language understanding (SLU) systems have many advantages over conventional pipeline systems, but collecting in-domain speech data to train an end-to-end system i…
Show Us the Way: Learning to Manage Dialog from Demonstrations
Gabriel Gordon-Hall, Philip John Gorinski, Gerasimos Lampouras +1
We present our submission to the End-to-End Multi-Domain Dialog Challenge Track of the Eighth Dialog System Technology Challenge. Our proposed dialog system adopts a pipeline archi…
Learning Dialog Policies from Weak Demonstrations
Gabriel Gordon-Hall, Philip John Gorinski, Shay B. Cohen
Deep reinforcement learning is a promising approach to training a dialog manager, but current methods struggle with the large state and action spaces of multi-domain dialog systems…