Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks

• URL: http://arxiv.org/abs/2110.15724v1
• Date: Sun, 10 Oct 2021 15:27:45 GMT
• Title: Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks
• Authors: Janarthanan Rajendran, Jonathan K. Kummerfeld, Satinder Singh
• Abstract summary: We show that we can use only a small amount of data, supplemented with data from a related dialog task. We describe a meta-learning based method that selectively learns from the related dialog task data.
• Score: 33.77022912718379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For each goal-oriented dialog task of interest, large amounts of data need to be collected for end-to-end learning of a neural dialog system. Collecting that data is a costly and time-consuming process. Instead, we show that we can use only a small amount of data, supplemented with data from a related dialog task. Naively learning from related data fails to improve performance as the related data can be inconsistent with the target task. We describe a meta-learning based method that selectively learns from the related dialog task data. Our approach leads to significant accuracy improvements in an example dialog task.

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