Presentation # | 7 |
Session: | Dialogue |
Session Time: | Thursday, December 20, 10:00 - 12:00 |
Presentation Time: | Thursday, December 20, 10:00 - 12:00 |
Presentation: |
Poster
|
Topic: |
Spoken dialog systems: |
Paper Title: |
Accumulating Conversational Skills using Continual Learning |
Authors: |
Sungjin Lee; Microsoft Research | | |
Abstract: |
While neural conversational models have led to promising advances in reducing hand-crafted features and errors induced by the traditional complex system architecture, training neural models from scratch requires an enormous amount of data. If pre-trained models can be reused when they have many things in common with a new task, we can significantly cut down the amount of required data. To achieve this goal, we adopt a neural continual learning algorithm to allow a conversational agent to accumulate skills across different tasks in a data-efficient way. We present preliminary results on conversational skill accumulation on multiple task-oriented domains. |