@inproceedings{rajendran-etal-2021-learning,
title = "Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks",
author = "Rajendran, Janarthanan and
Kummerfeld, Jonathan K. and
Baveja, Satinder",
editor = "Papangelis, Alexandros and
Budzianowski, Pawe{\l} and
Liu, Bing and
Nouri, Elnaz and
Rastogi, Abhinav and
Chen, Yun-Nung",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4convai-1.16",
doi = "10.18653/v1/2021.nlp4convai-1.16",
pages = "163--178",
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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%0 Conference Proceedings
%T Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks
%A Rajendran, Janarthanan
%A Kummerfeld, Jonathan K.
%A Baveja, Satinder
%Y Papangelis, Alexandros
%Y Budzianowski, Paweł
%Y Liu, Bing
%Y Nouri, Elnaz
%Y Rastogi, Abhinav
%Y Chen, Yun-Nung
%S Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F rajendran-etal-2021-learning
%X 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.
%R 10.18653/v1/2021.nlp4convai-1.16
%U https://aclanthology.org/2021.nlp4convai-1.16
%U https://doi.org/10.18653/v1/2021.nlp4convai-1.16
%P 163-178
Markdown (Informal)
[Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks](https://aclanthology.org/2021.nlp4convai-1.16) (Rajendran et al., NLP4ConvAI 2021)
ACL