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.
@article{arxiv.2110.15724,
title = {Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks},
author = {Janarthanan Rajendran and Jonathan K. Kummerfeld and Satinder Singh},
journal= {arXiv preprint arXiv:2110.15724},
year = {2021}
}